Severe and chronic fatigue is a prevalent symptom in multiple chronic conditions. Its complexity, its multifaceted nature and its varied manifestations across different conditions require a nuanced approach for accurate assessment by healthcare professionals. In our research, informatics-savvy public health nurses from a Digital Health Services and Health Promotion Master’s program evaluated various visualizations of patient-generated health data which could potentially be collected through a mobile app designed for people with Multiple Sclerosis. The data visualization prototypes could be a tool to support fatigue assessment and effective communication during consultations and their design was based on prior suggestions from healthcare professionals with experience in fatigue assessment. The patient-generated health data represented in the prototypes comprised a combination of fatigue-related factors and physical activity tracked by Google Fit. This study presents the recommendations of the participants regarding various aspects linked to the visualizations of patient-generated health data, including their utility in the clinical setting, the most suitable types of data summaries, usability aspects and the possibility of meaningful interrelations between distinct types of data. The results of our study emphasize the importance of well-designed data visualizations to support healthcare professionals in decision-making and to improve patient participation in the chronic care process. The iterative design process of the prototypes ensures that the final visualizations have proper usability and the potential to become clinically relevant, and instrumental in the effective assessment of fatigue in chronic management.
1 Introduction
Severe and chronic fatigue is a prevalent symptom observed in a variety of chronic conditions e.g. chronic obstructive pulmonary disease, type I diabetes mellitus, asthma, and multiple sclerosis. Its assessment can vary depending on the context, the underlying condition, and the patient's overall health [1]. Due to its multifactorial causes, accurate assessment of fatigue demands a blended approach which combines multiple methods such as patient-reported outcome measures, clinical interviews, physical examination, and review of symptoms and medical history.
The increased acceptance of mobile health (mHealth) apps and tracking devices among individuals with chronic conditions presents a unique opportunity to integrate these innovative tools into the healthcare processes. The data produced through wearable devices and mobile health applications can be utilized both to empower people to monitor their health and to enhance the healthcare process [2]. Patient-generated health data (PGHD) can be a way to understand patients’ environments and self-perceived states of health. Now, patients can record data and report health outcomes whenever and wherever they happen. However, this ubiquitous data collection increases the complexity of data security and data analysis and visualization. A major challenge in dealing with such a large volume of complex data is interpreting and extracting useful knowledge about users’ health [3] and research about effective methods to represent PGHD to cater for the diverse needs of users is still in the early stage of design [4].
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Understanding the status of a person’s health and the underlying factors behind the data produced by mHealth solutions is not a simple task [5]. In the case of fatigue, measurement and impact on patients’ lives still pose a challenge. Fatigue represents a widespread symptom reported by 90% of people with Multiple Sclerosis (PwMS), making it one of the most frequent and disabling symptoms of that condition. Assessing the impact fatigue has on patients’ lives can support healthcare professionals (HCPs) in their efforts to personalise care to the specific needs of each patient. The most typical goal for the visualization of symptoms data is understanding the relationships of symptoms to disease processes and their symptoms or treatments [6]. Previous studies have shown that patients find visual representations of their health data beneficial because they allow them to better recall past symptoms and relayed clinically relevant patterns that might be difficult to describe, especially when the current health status is not representative of the patient’s experiences over previous weeks or months [7].
Investigating how individuals interpret their self-generated data is important [4], especially if this data could potentially aid in improving care. Although previous research has explored efficient and effective ways to present data visualizations for lay audiences, similar research on PGHD from mobile technologies for the clinical context is limited [4]. In general, data visualizations play a significant role in helping different users comprehend data, facilitate communication, and support decision-making processes. Visualizations are also known to lower the cognitive load, enabling users to quickly understand the data [4]. In the clinical setting, effective data visualizations could assist HCPs in chronic care management, enhancing communication and decision-making, while facilitating data analysis, thereby benefiting the patient.
The primary objective of this study is to evaluate the efficacy of various data visualization prototypes designed to present patient-reported data on fatigue in a manner that is clear, concise, and actionable for HCPs. This paper seeks to identify the specific insights and requirements of experiences and informatics-savvy HCPs in assessing patient fatigue, to improve the development of visual representations of fatigue-related factors based on those needs, and to empirically validate the utility of these visualizations in the clinical setting.
2 Methods
To achieve our objective, we employed the focus group methodology with a group of HCPs, who at the time of the study were enrolled in a Digital Health Services and Health Promotion Master’s program. A previously defined guide led the execution of the focus group. The design of the evaluated prototypes utilized findings from previous participatory research. In a prior study, HCPs experienced with fatigue assessment created paper prototypes of PGHD that they considered could be useful in the clinical setting[8]. The visualization prototypes were based on the data generated by the “More Stamina” mobile app, which is a self-management solution designed for efficient energy management [9]. The solution was designed to support PwMS in self-management, leveraging wearable sensors and PGHD to provide personalized recommendations and, through anonymized data collection, aids in identifying patterns in living with fatigue [10].
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2.1 Development of the Visualization Prototypes
Teams of experienced HCPs, specialized in the care of PwMS, collaborated to develop paper prototypes for the visualization of PGHD collected through the More Stamina app. The prototypes aimed to leverage the use of PGHD during regular consultations, to enhance patient-clinician communication and monitoring in a concise format, adhering to a review time of five minutes or less. The details of the initial prototyping process have been documented in a previous publication [8]. Basic design principles were later used to improve the data visualization prototypes. The type and objectives for the different data visualization prototypes are explained in Table 1.
Utility of PGHD on Energy Expenditure
The use of mHealth solutions for the self-management of patients with chronic disease has been explored and positive results have been found, including the improvement of symptoms in conditions such as asthma, chronic pulmonary diseases, diabetes, and hypertension [2] and the improvement of pain and fatigue outcomes in cancer survivors [11], yet the potential of using the data generated by these solutions in the clinical context remain vastly underexploited. To evaluate the efficacy of patient-reported energy levels and how they are related to fatigue, we designed various data visualisations of energy expenditure by tasks and periods. A sample prototype is shown in Fig. 1.
Fig. 1.
Sample visualizations of energy expenditure by day and month.
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Periodicity of Visual Data Summaries
In a prior study, we identified that HCPs felt summaries related to physical activity and changes in energy levels during specific periods could give them a better understanding of factors affecting their patients in between appointments[12]. Considering the scarce time availability HCPs have during appointments, it was pertinent to evaluate whether summaries would be more effective on a daily, weekly, or monthly basis, or since the last appointment had occurred. To explore potential differences, we designed variations of data visualisations of energy expenditure of different periods.
Table 1.
Types of visualizations assessed.
Type of Visualization
Explanation
The objective of the Evaluation
Daily energy expenditure
Representation of the energy different activities consume and how they affect the daily energy level
To validate if a patient’s daily energy expenditure would be an understandable measure for HCPs
Daily and Monthly energy trends with Google Fit data
Representation of patient-reported energy level against activity tracked in Google Fit
To validate if the combination of patient-reported data with the passively tracked on a longer period would be useful for HCPs
Summaries of energy expenditure by activities
Various representations of patient-reported energy usage by type of activity and period (day, week, and month)
To validate in which periods summaries of patient-reported energy expenditure would be more meaningful for HCPs
Monthly activity level tracked by Google Fit
Various visual representations of activity tracked by Google Fit in a month
To validate if specific colour schemes and designs would facilitate the identification of high and low activity levels
Usability of Data Visualizations and Combination of Types of Data
Usability evaluations are of utmost importance to ensure efficient use of, suitable workload, and acceptance of healthcare technologies in the clinical context, [13]. Prior research supports the validity of the Google Fit smartphone application in estimating the stepping activity of individuals with chronic stroke suggesting it is a cost-effective alternative which could be suitable for the clinical context [14]. We designed data visualizations to evaluate if representing patient-reported data generated in combination with the passively tracked data from Google Fit would be useful for HCPs in fatigue assessment.
2.2 Implementation of the Focus Group Methodology
The focus group was conducted during an Innovation Workshop organized by Lapland University of Applied Sciences, which has recently intensified its cooperation with companies related to well-being, health technology and digital solutions in the social and healthcare sectors. Due to its densely sparse distances, Lapland offers an interesting value-adding environment for piloting digital solutions related to well-being and health.
During the workshop, the participants were first introduced to the distinctive features of the More Stamina app by the first author. Afterwards, the focus group was facilitated by the first and second authors, who guided the participants through the different visualization prototypes and related discussions. After participants had the opportunity to write down their observations on each prototype, discussion allowed for further understanding of the participants’ perceptions on each topic At the end, participants completed an exit questionnaire about their current utilization of mobile technologies and perception of PGHD. This focus group provided a good opportunity to understand the experience of first-time potential users, who were able to analyse each prototype and provide feedback as experts in the healthcare context.
No personal data was collected throughout the study and before the evaluation was conducted, all participants provided informed consent for their participation in the session and potential utilization of the results for research purposes.
3 Results
3.1 Descriptive Statistics
The participants of our study were nine public health nurses, students in a Master´s program in Digital Health Services and Health Promotion. All of them were female and the average years of working experience was 10.3 (SD = 6.24). Ownership of activity trackers was 85.7% and 100% for mobile phones.
3.2 Perception of Data Visualizations Prototypes
The participants found the visualization prototypes were clear and comprehensible, particularly for HCPs familiar with the topic. The visualizations were effective in representing energy expenditure. However, participants suggested that additional context would be helpful for HCPs less familiar with the specific data. This could be particularly beneficial for public health nurses who collaborate with a diverse patient base and require more detailed background information to interpret the visualizations accurately.
Utility of PGHD on Energy Expenditure
HCPs recognised the value of these data visualizations in understanding the relationship between patient activity levels and energy expenditure. Participants proposed that such a tool could be used to evaluate the effects of medications on patients’ fatigue levels, and potentially aid in treatment adjustments or preparation of rehabilitation plans.
Moreover, long-term data summaries were perceived as beneficial for setting realistic patient goals and customizing care plans, identifying energy expenditure patterns related to various activities.
Periodicity of Visual Data Summaries
Although HCPs deemed detailed be essential for initial patient assessments and establishing patterns, in the long term, their preference leaned towards summarized data due to its clarity and practicality. Summarized data, whether weekly or monthly, seemed more practical for monitoring and providing meaningful insights without the intensity of daily analysis. Monthly summaries, or a summary since the last check-up were more suitable for ongoing patient evaluations and routine assessments. HCPs indicated this preference may vary depending on the clinical role or the specific needs of the patient; However, 22% of our participants noted that daily summaries could be useful for HCPs at certain points in the care process.
Fig. 2.
Sample summarization of activity points tracked with Google Fit in a month.
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Usability of Data Visualization and Combination of Types of Data
HCPs reviewed various visualizations which integrated data from the Google Fit application, each with distinct design elements and colour schemes. A unanimous preference emerged for a specific prototype, which was deemed the most effective in illustrating patient activity levels. Participants felt this design intuitively facilitated the identification of varying activity intensities (see Fig. 2).
3.3 Support in Understanding Patients
From the responses to the exit questionnaire, we identified all participants agreed on the value of PGHD from health apps and activity trackers in comprehending patient conditions. Despite over half (55%) of them not having previously recommended mHealth apps to patients, they acknowledged the potential of integrating PGHD with conventional clinical data for a comprehensive understanding of patient health particularly when patients are discharged from the hospital, to understand how they continue at home by themselves. Additionally, almost half of the participants (44%) reported an increase in patient interactions which involved patients wanting to share data from mHealth solutions, reflecting a growing trend in patient-driven health data sharing.
4 Discussion
Our study revealed a notable trend: patients are increasingly using mHealth solutions and are willing to share their health data with HCPs to support treatment effectiveness. This can be related to prior studies where participants reported using visualizations from their self-tracking apps to foster higher-quality dialogue with their care team [7].
Effective visualization of PGHD requires a thorough design that aligns with specific data types and the intended functions of the data visualization [4]. Our approach to developing the visualization prototypes was iterative, initially led by HCPs skilled in fatigue assessment with PwMS. The initial designs were refined using basic design principles, resulting in prototypes that were well-received by our participants. However, the feedback obtained during the focus group highlighted the need to include more descriptive elements to enhance the usability of the visualizations to understand PGHD effectively.
In designing PGHD visualizations for HCPs it is crucial to recognize that patients, as the primary users of self-management mHealth solutions, are willing to spend time reviewing their health data. Conversely HCPs, as secondary users, may benefit from PGHD to support their work but do not require the same level of detail as patients, since they already utilize various clinical methods for fatigue assessment. Simple and efficient visualizations significantly enhance HCPs’ ability to access and comprehend data[15] allowing for increased patient involvement. HCPs highlighted the importance of clarity and intuitive design for those familiar and unfamiliar with the. This underscores the need for accompanying explanations or legends that can provide the necessary background to understand the visualized data. This is pivotal in clinical settings, where time constraints demand clear, concise information that can critically influence patient care and decision-making.
The participants agreed on the potential for PGHD visualizations to enhance various aspects of patient care, from preventive health to treatment evaluation. The visualizations were viewed as tools to gain a more nuanced understanding of the patient's condition, which can lead to more informed decision-making and personalized care strategies. HCPs indicated that summarized data, whether weekly or monthly, seemed more practical for monitoring, yet they remarked on the need for adaptability in the frequency and depth of data summaries, striking a balance between detail and usability to effectively represent the patient's overall health status.
5 Limitations
The present study, while comprehensive in its approach to evaluating data visualization prototypes of PGHD for HCPs, has limitations that should be acknowledged. First, the study focused on patient-reported fatigue data generated by a mHealth solution for PwMS. Fatigue, while a common symptom across various chronic conditions, can manifest differently in different conditions and patients, requiring different approaches for accurate assessment. Therefore, further research would be required to validate whether patient-reported fatigue data generated by people with other conditions would be as meaningful when visualized in a similar way.
Furthermore, the study does not extensively address potential data privacy concerns or the regulatory implications related to utilizing PGHD in clinical decision-making. The integration of PGHD into the healthcare process must be conducted with consideration for patients’ data security and compliance with the corresponding regulations. However, the study's main objective is to evaluate the potential of PGHD visualizations as a supplementary tool for clinical practice. The use of this type of tool would imply the active participation and collaboration of patients. Potential data privacy concerns and regulatory matters need to be addressed in more detail in future studies.
In addition, while the study provides insights into the perceived utility of the proposed data visualizations, the long-term benefits, and potential risks or drawbacks of using these tools in chronic care management could not have been explored extensively using our study design and context. Future research would be required to assess the long-term impact and potential challenges of implementing these tools in clinical practice.
6 Conclusions
The utilisation of mHealth apps like “More Stamina” and the integration of patient-reported fatigue with passively collected data from tools such as Google Fit highlight the potential of PGHD for improving the assessment of severe and chronic fatigue.
Our research has shown that HCPs can leverage data visualizations of PGHD to gain a more nuanced appreciation of patients’ energy levels and activity patterns. This could inform more personalized and effective treatment plans, medication management, and rehabilitation programs. The feedback on our data visualization prototypes was positive, suggesting that the data representations were understandable and potentially effective for HCPs; however, further research in varied clinical settings would be necessary to validate these results and assess their utility in the treatment of other health conditions.
The iterative design and evaluation process for the data visualization prototypes is a step in the right direction, as it aligns the development of mHealth solutions that can cater for the needs and preferences of different users. As healthcare systems continue to evolve with technological innovations, integrating mHealth solutions into the clinical practice holds significant promise for improving patient outcomes and healthcare experiences by leveraging the use of PGHD.
Acknowledgments
The authors would like to thank Milla Immonen, Anu Kinnunen, Guido Giunti, Vasiliki Mylonopoulou and Octavio Rivera Romero. Without your contributions, this study would not have been possible.
Disclosure of Interests
The authors have no competing interests to declare, that are relevant to the content of this article.
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