How should a symptom self-tracking system for people with Parkinson’s Disease be designed for optimal usability?

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How should a symptom self-tracking system for people

with Parkinson’s Disease be designed for optimal

usability?

Bachelor thesis Information Science

Faculty of Science - University of Amsterdam

Annejet Robijn

11269111

07-07-2019

Supervisor: dr. F. Nack

2

nd

_{Examinor: M. Kern Msc}

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Abstract

People who have been diagnosed with Parkinson’s Disease (PD) will experience a broad range of symptoms, with a wide variety in severity and type among the entire PD population. Well known motor symptoms include slowness of movement (bradykinesia), resting tremor and balance and gait difficulties. The disease is difficult, if not impossible to diagnose with a 100% certainty, as there are no biomarkers, however, personal data may help identify patterns in symptom developments. In self-tracking, people regularly track any data about the self, mostly with the goal to improve their health. Therefore, in this thesis was explored how a PD symptom self-tracking system should be designed for optimal usability. Based on a research review and two interviews, three requirements for an app were defined: flexibility, freedom, and usability. With these in mind, a clickable demo was created. In this demo, the user has the option to add data about a new workout, do a dexterity test, and view their progress over time. To measure the perceived usability of the demo, a System Usability Scale (SUS) test has been completed by 12 participants. This resulted in an average score of 77.92, which can be interpreted as an acceptable score, meaning the usability is perceived as good to excellent. Based on the results, it can be concluded that the average perceived usability by PwP of the clickable demo is good to excellent. In addition, the perceived usability of the clickable demo is lower in older participants. As a result, we can say a symptom self-tracking system for people with Parkinson’s should comply with the three requirements that were defined in this thesis. Limitations to this thesis are the small number of participants (N=12), and the setting in which the SUS test was completed.

Keywords: Self-tracking, Parkinson’s Disease, Human-Computer Interaction, System

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1. Introduction

1.1 Parkinson’s Disease

People who have been diagnosed with Parkinson’s Disease (PD) will experience a broad range of symptoms, with a wide variety in severity and type among the entire PD population. Parkinson’s Disease is often referred to as ‘PD’, likewise in this thesis. PD is a neurodegenerative disease of which the cause is as of yet unknown, although research points in the direction of a combination of environmental and genetic factors (Michael J. Fox Foundation for Parkinson’s Research, 2019). The average age at diagnosis is 60. However, the percentage of people receiving the diagnosis under the age of 50 is estimated to be 10 to 15%. The symptoms people with Parkinson’s (PwP) experience are primarily caused by a loss of neurons in the substantia nigra, the dopamine producing cells in the brain. Dopamine is a neurotransmitter which plays an essential as well highly complex role in neurobiological and behavioural functions, such as memory, and movement regulation (Anon, 2014).

Well known motor symptoms include slowness of movement (bradykinesia), resting tremor and balance and gait difficulties. Lesser known non-motor symptoms are cognitive impairment, mood swings, sleeping disorders, the inability to multi-task and memory issues (ParkinsonNet.nl, 2019). However, no two PwP are alike, with each single PwP experiencing a unique set of symptoms, adding to the complexity of this disease. There is no standard treatment. However, most treatments consist of medication, surgical therapy and lifestyle changes, such as increased exercise. Often, a variety of medication has to be taken, all at different doses and at different times (Parkinson’s Foundation, 2019). PD is a progressive disease for which there is no cure. Symptoms will worsen over time (the Michael J. Fox Foundation for Parkinson’s Research, 2019). To understand the progression of PD, patterns are defined in five different stages (Parkinson’s Foundation, 2019). At the initial stage, a PwP will experience very mild symptoms that generally don’t interfere with daily activities. At later stages, the so called PD 4 and PD 5 Levels, PD may cause severe disability and dependency.

1.2 Self-tracking

For ages, people have been monitoring elements of one’s body and life, aiming to improve or reflect on the self (Lupton, 2016). Currently, this phenomenon has become known as ‘self-tracking’ or the ‘quantified self (QS)’. Self-tracking and QS have gained new interest thanks to the introduction of technology. In 2007, the Quantified Self movement was founded, a movement in which technology is central (Quantified Self Institute, 2018). QS embodies self-knowledge through self-tracking, where people regularly track any data about the self (Swan, 2009). Millennials, among all age groups, are the most active when it comes to self-monitoring (Paré et. al, 2018). Some people collect data to record aspects of their lives, hoping to discover physical or behavioural patterns. Others engage in self-tracking with a clear goal-oriented view, such as the desire to improve their health status, their physical fitness, emotional well-being, social relationships or work productivity (Lupton, 2016).

Two types of self-tracking related to health can be defined: physician-initiated tracking and patient-initiated tracking (Gimpel et. al, 2013). In the case of physician-initiated tracking, physicians ask their patients to track certain aspects of their health and to make the data collected available to the physician, aiming to provide more regular and continuous information (Trueman, 2009). In patient-initiated tracking, it is the patient who initiates the self-tracking, collecting valuable health information which is accessible and usable by the patients themselves. Measurable parameters often include disease symptoms, medication, body weight, sleep habits, blood test results, blood pressure, nutrition, habits, and mood (Gimpel et. al, 2013). In research by Fahrenberg et. al (2007) other relevant vital parameters

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such as respiration, skin temperature and movements are specified. The monitoring of patients in their daily environment has been found to be very effective in managing symptoms, especially for those diagnosed with chronic diseases (Gimpel et. Al, 2013).

1.3 Purpose and Relevance

An example of a chronic disease in which patients may benefit from self-tracking, is Parkinson’s Disease. The disease is difficult, if not impossible to diagnose with a 100% certainty, as there are no biomarkers, however, personal data may help identify patterns in symptom developments. Dr. Little explores the possibilities of smartphones to gather data on changing symptoms of the disease (BritishCouncil.org.tr, 2019). The associated symptoms and progress vary widely for each person individually. People who are diagnosed with this neurodegenerative disease all experience their symptoms differently, so it would be useful to know if there is an efficient way to accurately track symptoms.

Huckman and Stern (2018) did research into the current challenges of self-tracking of chronic diseases through apps. One of the issues is that people do not see managing their disease as a leisure activity, making it harder to track on a regular basis. Moreover, personal medical data does not just consist quantifiable facts, as it also involves emotions, value judgements and differences in interpretation as well as context. Other problems arise when it comes to access to the data by the patient who is self-tracking. A good example is the mPower app, developed by Apple (mPower, 2019), and specifically designed for PwP. The application was actually a 2 year mobile research study into understanding the progression of PD. The mPower app does not allow its user to access their captured data. In the meantime, the number of available tracking tools is increasing rapidly, but people still struggle to find a tool that perfectly suits their individual tracking needs and goals (Lazar et. al, 2015). People use survey tools such like Google Forms (Google, 2019) instead, because they allow for diverse customization (Kim et. al, 2017). Besides, Lazar et. al (2015) found that many tracking systems allow people to track multiple behaviours, but the set of behaviours is mostly predefined by developers rather than by end-users. This limits the freedom of a user to track exactly those symptoms they wish to track.

As far as visits to a physician are concerned, people suffering from a chronic disease often see their physician a limited amount of times each year, which means patients mostly rely on self-care (Palfreman (2014), Jauhiainen et. al (2019)). This also implies that the physician has a limited view on how the patient is doing outside visits. This where self-tracking comes in. Self-tracking has the potential to give people more insight into the development of their symptoms over time and how they manage these symptoms. People with chronic diseases who are self-tracking will also be more likely to become engaged as advocates for their personal care. Self-tracking may well enable patients to regain control over their symptoms, increasing chances they will sustain new and healthier behaviours. Health care providers on the other hand, may benefit from self-tracking as well, thanks to their access to real world data of their patients’ daily activity patterns (Chiauzzi et al. 2015). Through these technologies, the information flow between patient and physicist will increase, improving health care collaboration (Swan, 2009). A physician who has access to more data on his or her patients, will be more likely to give these patients more relevant as well as accurate advice.

Therefore, it is relevant to do research into self-tracking in PD in order to define which tracking parameters should be applied, and which method of self-tracking is useful to PwP. This will be explored in order to understand how people currently manage and track their symptoms and how a self-tracking system could support them in doing so, focussing on optimal usability.

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The goal of this thesis has been narrowed down to understand how a self-tracking system for PwP should be designed in order to be user-friendly, which may also point in the direction of future research in this area. In result, the question that will be answered is as follows: “How should a symptom self-tracking system for people with Parkinson’s Disease be designed for optimal usability?”. The results of this thesis as well as results of future research is hoping to fill the gap between the self-tracking needs of PwP one the one hand and developers of self-tracking apps on the other. With 50.000 PwP in the Netherlands (Volksgezondheidenzorg.info, 2019) alone and an estimated amount of 10 million worldwide (Parkinson Foundation, 2019), this is a topic that concerns millions of PwP and their relatives.

I expect that PwP will value a tracking application with a simple, clear design, which has buttons that are very easy to click. In addition, I expect users to value a lot of freedom in choosing which parameters they want to track. The remaining structure of this paper is as follows. First, literature about self-tracking systems and healthcare will be reviewed. After that, interviews will be done with PwP on their experience with available self-tracking systems. Based on the literature review and two interviews, the most important parameters to measure PD symptoms can be determined. With these in mind, a clickable demo of a self-tracking system will be created. Next, PwP will be asked to test the usability of the application with a System Usability Scale test and to give feedback on their interaction with the demo.

2. Theoretical Framework

How do self-tracking systems benefit their user? In this section, various problems and opportunities regarding those systems will be discussed, in order to gain understanding of why and how people practice self-tracking. PwP may define other requirements for a PD tracking app than developers of those apps (Lazar et. al, 2015). Therefore, to create a clickable demo for PwP that meets their wants and needs, it is useful to get an overview of the problems that occur in current apps, to define requirements for a PD symptom self-tracking app. In support of that, two interviews with PwP are done.

2.1 Literature Review: Self-tracking systems & Healthcare

According to Chung et. al (2016), self-tracking data may benefit healthcare providers in five major areas, the first of which is ensuring people receive a correct and timely diagnosis. Other areas are personalizing treatment, learning more about patients, and managing visits. Patient generated data may also increase the motivation and accountability of patients, if they receive the right instructions for the self-tracking tool and get feedback on their data. For patients, it is an advantage to be able to automatically store data that is collected from wearable sensors and smart devices so that they don’t have to memorize each and every behaviour by heart anymore (Paré et. al, 2018). However, providers of self-tracking apps also mentioned that in current self-tracking systems they experienced difficulty with effectively using the data. According to Espay et. al (2019), current tracking systems for PD that make use of wearable sensors, still encounter some challenges. Many systems monitoring the development of chronic diseases are not compatible with one another. This makes it hard to review, compare and analyse data from different sources. Besides, the use of many different sensors can feel intrusive to the individual who is tracking and measuring.

Nonetheless, there are still many opportunities to overcome these and other issues. For example, increasing the interaction between patient and clinician through self-tracking systems has the potential to further personalize healthcare (Appelboom, 2014) and might even decrease the need for in-person visits (Espay et. al, 2019). The ability to do measurements

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using smart devices, like mobile phones, smartwatches and tablets, provides opportunities to collect more relevant parameters to track Parkinson’s symptoms, including posture, gait, balance, speech patterns, eye tracking, dexterity and facial expression (Maetzler et. al, 2016). These devices also allow for the development of communication features in those systems, which may improve patient engagement (Espay et. al, 2019).

This engagement may improve motivation for using an application. Extensive research has been done into the motivations of the Quantified Self movement by Paré et. al (2018) amongst the adult population of Canada. They define three tracking profiles: digital self-trackers, who regularly monitor themselves using care technologies, traditional self-self-trackers, who track using manual tools like a journal, and non-trackers, who do not regularly track any aspect of their health. In general, self-tracking systems are used to sustain individual well-being. Common motivations in all profiles were to increase knowledge of one’s condition and to monitor changes in health status, helping people to achieve personal goals, and to monitor fitness improvement. The traditional self-trackers are mostly aged 55 years and over and are more likely to suffer from a chronic disease than the other groups. They also tend to monitor parameters that are relevant to chronic conditions. Digital self-trackers make up for two third of all trackers, and are mainly young and highly educated. The common motivation amongst this group is to monitor fitness behaviours and goals. Non-trackers are of all ages and mostly lower educated. They believe the information provided by their physician is sufficient or they simply don’t feel the need to track symptoms.

Yet, trackers with a chronical condition may consider self-tracking to be an unwelcome core. A positive and rewarding user experience could aleviate this, for example by adding gaming elements to the application. A study amongst students’ interest in self-tracking (Harari et. al , 2017) points out that most of them start off with the ambition to improve their productivity and physical or mental health. Improvement of health is one of the major reasons for people to start using tracking systems, as stated in research from i.a. Choe et. al (2014), Kelley et. al (2017), and Paré et. al (2018). In addition, various aspects of one’s life and finding new life experiences can play a role in this (Choe et. al, 2014). Initial motivations also arise from the desire to change behaviour, reach certain goals, gain motivation, instrumentation or simply from the wish to keep up with new technologies (Epstein et. al, 2015).

Satisfaction with the technology is decisive in whether a user continues using self-tracking devices, which in itself is influenced by the user’s expectation of the app. Users who stop tracking, mostly do so because of doubts about data reliability, malfunctioning of the device or just loss of interest (Paré et. al, 2018). Not actually owning a tracking device is mostly because of financial reasons and lack of knowledge about its value and reliability (Paré et. al, 2018). Other pitfalls for people engaged in self-tracking are ‘over-tracking’: tracking too many things at the same time and just tracking symptoms instead of triggers and context. Over-tracking may produce data that is not or not entirely useful for patients (Choe et. al, 2014). Limitations to current tracking systems can be that users simply forget to track or forget to keep up with the maintenance of the device, such as charging its battery, skip tracking because it takes too much effort, or suspend tracking altogether for a period of time when they do not need or want the system (Epstein et al, 2015). Manual tracking can engage people during the data capture, which enhances their awareness. On the other hand, it could impair the accuracy of results. Automated tracking has the potential to reduce effort required for capturing data, while at the same time increasing the risk of people not being aware of it (Lazar et. al, 2015). Furthermore, tracking and reviewing data can be an emotional burden and may be confronting, as the data regarding a persons disease development is being visualized (Paré et. al, 2018).

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In PD, tremor and other motor complications can be detected by using biomechanical sensors. Yet, the data collected this way does not always qualify for a reliable clinical assessment of the symptoms. It is difficult to infer from this data alone whether slowness of movement in PwP is an indicator for bradykinesia or just the result of fatigue or other factors of the context in which the task is being performed (Espay et. al, 2019).

Stawarz et. al (2015) state that in order to change behaviour on a long-term basis, new behaviour should become automated, until the habit has actually established. Smartphone apps may support people in this area. Reminder notifications from apps support repetition, but do not lead to habit formation. Event-based cues on the other hand, may well lead to increased automaticity. This feature could be implemented in a self-tracking system. For a user to automatically track certain parameters, event-based cues could be of help. Another element that has been found effective is gamification, where game play is incorporated into non-game situations. Gamification has a positive influence on motivation (Friedrich et. al, 1973) and can be used to engage people to accomplish tasks (Prince, 2013). Regularly tracking activity is also motivated by receiving awards after completing certain tasks (Epstein et. al, 2015). Additionally, the setting of a tracking system plays a role in its the effectiveness. Online self-tracking in a group setting is found to be more effective than self-self-tracking individually, concerning food intake monitoring (Meng 2017).

2.2 Interviews with PwP on available self-tracking

Swedish health care researcher, PD Research Advocate and PwP Sara Riggare examines the use of personal observations for improvement in chronic diseases at the Karolinska Institute in Stockholm (BMJ, 2019). Sara Riggare experienced her first PD symptoms at the exceptionally young age of 13. The personal interview aimed to get Riggare’s opiniated view on self-tracking systems as well as her experiences with self-tracking systems. The transcription can be found in the Appendix 8.1.

Riggare brings up the mPower App, which was launched in 2014 (mPower, 2019), which she believes is well designed, but the app lacks a feature giving the user access to and control over their own data (S. Riggare, personal communication, June 10, 2019). In general, Riggare states, most apps that are currently on the market are not holistic enough, meaning that these apps do not take into account the full complexity of PD. For example, the application Beats Medical (Beats Medical, 2019) offers exercises for speech, mobility and dexterity. However, this does not take into account other symptoms of PD, such as cognitive impairment and memory issues. The app Peak (Peak, 2019) though, does offer exercises for this, but focuses on just those exercises. Parkinson’s Toolkit (National Parkinson’s Foundation, 2019) only offers symptom tracking for the most common symptoms.

In a personal interview with another PwP, Mariëtte Robijn (personal communication, April 1, 2019) also mentions the holistic apps. Robijn (diagnosed at 46, now 53) is engaged in PD Research Advocacy (Parkinson’s Research Advocacy Group, 2019) and she is a PD blogger. The transcription of the interview with Robijn can be found in the Appendix 8.2. Robijn emphasizes the importance of exercise for PwP, expressing the need for PD specific exercise self-tracking systems.

Inflexibility of self-tracking apps

Riggare points out that current self-tracking apps are not very flexible. Users can only measure what others have decided for them to measure. The same problem was stated earlier by Lazar et. al (2015). This would also imply that apps are not entirely patient-centric. People with Parkinson’s may differ from health care professionals in how they value the usefulness of

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parameters that are being measured. Therefore, the first requirement (R1) is flexibility of the self-tracking app.

Continuous tracking

Both Riggare and Robijn point out that where most apps assume PwP want to track their symptoms continuously, in reality most PwP do not want to track their symptoms non-stop at all. Riggare and Robijn believe that many PwP do not want to track continuously, as this would be a constant reminder of their condition. This results in the second requirement (R2) which is a user’s freedom to exactly choose what and when to track .

Usability

Usability of the tracking system is crucial for PwP to start and keep using an application. For PwP in particular, Robijn states, buttons and clickables should be clearly notable, easy to reach/navigate with large displays and the design will have to be simple. Motivation to use a tracking app would be increased if the user receives awards and positive feedback, Robijn thinks. Riggare also emphasizes the usability of current self-tracking systems. Some are too complex for a number of PwP to be able to understand the app and its features. Adding to that, current tracking applications often lack access to and usability of the collected data. For many people, raw data may be hard to interpret, especially when the user is lacking expert knowledge. In this respect, Riggare mentions the Apple Watch (Apple, 2019), which has the ability to perform a very accurate ECG. However, “If you can’t understand the data, there’s not much you can do with it.” (Riggare, personal communication, June 10, 2019). Not all self-tracking system users are able to interpret their data correctly, moreover, application developers cannot expect all PwP to be able to do so. Raggare emphasizes that data that is collected should be easily accessible as well as interpretable for its entire target group. Therefore, a third requirement (R3) can be defined: high usability of the application.

The main point both Robijn and Riggare make, is that they would be very interested in a self-tracking system that gives its users full freedom. Such an app would have to enable users to choose from several modules, allowing them to select the parameters they want to track at any time. The data that is collected this way, would also have to be easily accessible, interpretable and convertible, so users can analyse and compare data themselves.

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3. Method

3.1 Clickable Demo

Based on the research review, interviews, and requirements stated before, a clickable demo was created (Appendix 7.3). The software used for this is called Marvel (Marvelapp.com, 2019), a tool especially made for wireframe, design and prototype. Due to the time limit, three features were chosen for implementation. On the home screen of the demo, the user has three options to click (Figure 1). This takes into account the freedom of the user to choose exactly what and when they want to track. The first is ‘New workout’ (Figure 2), where the user can log sport activities, and choose the corresponding duration, intensity, and date the activity was completed. The second option (Figure 3) is to view the users’ personal statistics, where they can see an overview of their test results and progress over a certain amount of time. The third option (Figure 4) is a dexterity test, which is done by alternatively tapping two fingers of the same hand as quickly as possible, also known as the finger tapping test. The interface of the finger tapping test is partly based on the existing app mPower (mPower, 2019). Through the rest of the interface, close attention was paid to a clear and simple design easy to click buttons. On the home page screen, there was also a small icon on the top right of the screen. By clicking this icon a screen explaining the app appears. The language used in the demo is Dutch, because all participants of the usability test were Dutch speaking.

Figure 2. Clickable Demo: Home Screen Figure 2. Clickable Demo: New Workout. Figure 3. Clickable Demo: Personal Statistics. Figure 4. Clickable Demo: Dexterity Test.

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3.2 System Usability Scale

To measure the perceived usability of the clickable demo, a System Usability Scale (SUS) has been used. This is a reliable tool, consisting of a 10 item questionnaire, with five response options for each question (Brooke, 1996). The questions are alternated by positive and negative statements in order to avoid response biases. Users will rank each question from 1 to 5, based on their level of agreement, from ‘strongly agree’ to ‘strongly disagree’. Next, the scores for each participant are normalized (Thomas, 2015). From odd numbered questions 1 will be subtracted from the score. The value of even numbered questions will be subtracted from 5. All these new values will be added up and multiplied by 2,5. The average SUS score is 68 (Brooke, 2013). Products are acceptable when their SUS score is above 70. A higher score represents a higher usability. The score can be interpreted as seen in figure 5.

A benefit of using the SUS is that it can be used on small sample sizes, while still returning reliable results. The sample size is relatively small in this thesis. A template for the test can be found in Appendix 7.4. All the questions have been translated to Dutch, because this was the native language of the participants.

The SUS was completed via Google Forms (2019) by 12 people, each of whom complied with the inclusion criteria of having Parkinson’s Disease and being able to use a smartphone to interact with the demo. The dropout rate was 0. Amongst the participants, seven were male and five were female. Their ages ranged from 51 to 79, with an average age of 67. The participants were all living in the Netherlands at moment of the test and were reached via the sports club Rock Steady Boxing Het Gooi (Parkinsonsport.nl, 2019). The test was completed on June 18th_{and June 20}th_{in Huizen, The Netherlands. On the first day, 1 participant from the}

boxing group completed the task before their training, and 6 of them did so afterwards. On the second day, 2 participants from the boxing group completed the task before their training. The other 3 participants that day were not member of the sports club.

The test started with a short explanation about the goal of the thesis and an introduction of the clickable demo and SUS test. Next, the participant interacted with the clickable demo until they had viewed all screens. The participant was completely free in what they wanted to click first and how many times they wanted to try a function. After they felt they had seen all the functions of the demo, they completed the questionnaire. This included the SUS test, an open space to provide any comments about the demo and two demographic questions about their age and gender.

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4. Results

4.1 SUS Scores

The mean SUS score for all participants (N=12) is 77,92, with a median of 83,75, and a range of 40 to 100. There are no scores below 40. The mean scores and age per participant can be viewed in table 1. On average, the participants were 67 years old.

Table 1. SUS score and age of participants.

Participa nt SUS score Age P1 90 62 P2 100 73 P3 55 72 P4 40 74 P5 85 79 P6 72,5 73 P7 80 61 P8 90 63 P9 82,5 51 P10 95 79 P11 87,5 53 P12 57,5 68

Participants in the category from age 50 to 60 had an average SUS score of 85 (table 2). Participants aged from 60 to 70 had an average of 79 and participants aged 70 or higher had an average score of 75. This shows that the older the participant, the lower they perceived the usability of the app.

Table 2. SUS score per age (years) group.

Age SUS Score

50 to 59 85 60 to 69 79 70 to 79 75

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Based on figure 5, four score groups were distinguished. The first group contains participants with a score below 60, which is not acceptable. The second group has scores between 60 to 79, which indicates an acceptable score. The third group is scored from 80 to 89, indicating good to excellent perceived usability. The fourth group consists of scores from 90 to 100, indicating excellent to best imaginable usability. In table 3, the distribution of the average age per score group can be viewed. Participants in the lowest score group had a mean age of 71,33. In the next group, consisting of people who perceived the clickable demo to be more usable, had an average age of 73. In the next two score groups, participants were younger of age, but had a higher SUS score.

Table 3. Amount of participants and average age (years) per score group.

Score group Participants Age

1 (0 to 59) 3 71

2 (60 to 79) 1 73

3 (80 to 89) 4 61

4 (90 to 100) 4 69

4.2 Comments and Observations

After the participants had answered the SUS questions, they were given the opportunity to submit any comment about their interaction with the demo. Other observations made while participants completed the test were also noted. What stood out, was that only one participant clicked the information button. Besides that, 6 people mentioned they thought the finger tapping test was interesting and user-friendly. However, not all participants liked the screen on which a new workout can be added. They thought the scale option for setting the intensity, duration and date of the workout not easy to use, especially for people that experience tremors. They would prefer a design where you can click on the numbers or type them. Besides that, all the clickables were found easy to tap. Others found the naming of ‘intensity’ and ‘duration’ a little confusing. Another observation made was that most participants did not automatically click on the button ‘next’ to start the finger tapping test, after reading the instructions above.

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5. Discussion

In this thesis was explored which tracking parameters should be applied in a symptom self-tracking system for PwP and which methods for this would be most useful for them. The goal was to understand how a self-tracking system for PwP should be designed in order to have an optimal perceived usability.

In total, 12 participants completed the SUS test, resulting in an average score of 77.92. Compared to the scoring model in figure 5, this can be interpreted as an acceptable score, with the usability of the demo being perceived as good to excellent. This implies that the perceived usability of the clickable demo that was designed based on the literature review and interviews, is good enough for PwP to use. This also means that the three defined requirements for a PD self-tracking app, were dependable requirements to base the design of the demo on.

Table 2 shows that the perceived usability lowers as participants become of older age. With a mean score of 85 for the youngest people in age group 50 to 59, which is considered more than excellent (figure 5). The oldest group, aged 70 to 79, had an average score of 75, which is considerably lower. However, a score of 75 still indicates a good perceived usability. Overall, it can be concluded that PwP perceive the demo as more usable if they are of younger age.

Table 3 shows that the participants in the lowest score group had an average age of 71, and the highest two score groups an average age of 61 and 69. Both these ages are lower on average, which could again imply that people with a younger age perceive the clickable demo as more usable than people with a higher average age. This complies with the results of table 2 and supports the hypothesis that perceived usability of the demo by PwP lowers as they become of older age. PD is a progressive disease, meaning that symptoms worsen over time (Parkinson’s Foundation, 2019). This generally causes older PwP to experience more or worse symptoms than younger PwP, which can explain the lower perceived usability of the demo at higher age.

The comments participants made after the test implied that the scale option was not usable. Some PwP experience tremors, which makes it harder for them to accurately press small buttons and adjust the scale. However, participants did like the interface of the finger tapping test. They found it easy to use as well as an interesting and entertaining way to track their symptoms. As mentioned before, the design of this test was inspired by the design of the mPower app (mPower, 2019). This application was developed specifically for research into progression of PD, which can explain the well perceived usability by the participants. This supports the hypothesis that PwP value a clear design with easy clickables.

Based on these results, the following can be concluded:

1. Average perceived usability by PwP of the clickable demo is good to excellent. 2. Perceived usability of the clickable demo is lower in older participants.

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Limitations

On a methodological view, different factors may have influenced the outcomes of this thesis. Firstly, 9 of the participants were reached via a sports club especially for PwP. These 9 participants were all in the same training group. Three of them completed the test before their training and six of them after. This may have influenced their interaction with the demo.

The people that work out in the boxing group all exercise regularly. Exercise has been found effective in delaying or reversing functional decline for PwP (Goodwin et. al, 2008). This implies that the respondents in this thesis might not be completely representative of the complete PwP population. Besides, every individual with Parkinson’s experiences a unique set of symptoms (Parkinson’s Foundation, 2019), so with a number of 12 participants, the representation of the complete PwP population might not be very accurate. However, SUS can be used on small sample sizes. The score of five participants would only differ by about 10 points for 95% of the time, compared to a very large sample size (Sauro, 2013).

Users who complete a SUS test and have a lot of experience with the application from before, will have a higher rate of perceived usability (Sauro, 2013). If some participants had more experience with self-tracking apps than others, this might have influenced their score.

Another factor that may have influenced the test results is the setting of the test. The participants did not all take the test in exactly the same setting. In addition, some participants needed more explanation than others. The researcher had to read out and explain some of the SUS answers for a few of the participants. Because the researcher sat next to the participant, there is a chance that participants did not feel free to say anything they wanted to.

The amount of participants may have limited the reliability of comparing age groups on their average SUS score. The youngest age group included only 2 participants, while the oldest age group included 6. This might not make it reliable to compare SUS mean scores between the different groups.

Conclusion and Future Work

In this thesis was explored how a symptom self-tracking system for PwP should it be designed for optimal usability. It can be concluded that such a self-tracking system should comply with the three requirements that were defined in this thesis: flexibility, freedom, and usability. However, older PwP might need other requirements. The requirements on which the design of the demo was based were dependable, but still quite broad. In future research, additional requirements as well as further narrowed down requirements are recommended. In addition, more participants are needed to get a more accurate view of the perceived usability amongst the entire PwP population. Next to that, research could be done over a longer period of time, to analyse whether people will actually use the tracking application. It would then also be useful to look at more parameters to implement in the demo. As Robijn and Riggare emphasized in their interviews, the optimal tracking app would include all the tracking parameters that can be useful to a PwP, while at the same time allowing its user to choose exactly what parameter to track as well as at what moment they want to do it.

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7. Appendix

7.1 Interview with Sara Riggare

Time: 16.00, 10-06-2019. Place: Amsterdam, Stockholm

Keywords: Self-tracking, Parkinson’s Disease, chronic disease management, healthcare

Interviewer: I looked through your website and read you experienced your first Parkinson’s symptoms from the age of 13, which is exceptionally early. I’m wondering; when you were just diagnosed, how did you start with managing PD?

Riggare: To be honest I didn’t at all. I didn’t know it was Parkinson’s I have until much later. Didn’t start medication until few years later either. I think what I did, is just avoid things I found difficult to do. Which probably isn’t the best thing, but that’s just what I did.

Interviewer: Do you practice self-tracking? And if so, when did you start and what way of tracking do you prefer?

Riggare: Yes I do, but only when I feel the need to track. I use Google Forms to make notes about my medication timings and other observations. I started maybe in 2011 or 2012. Depends on how you see it. From the first time I started medication, I started writing down how I reacted. So when I started medication, I started tracking as well. I started my medication in 1991.

Interviewer: Do you have any experience with using apps for self-tracking?

Riggare: I currently don’t use any apps for self-tracking. I don’t think any app is good enough for self-tracking for people with Parkinson’s. When I have the need for checking my medication timings, I use an app for tapping tests: finger tapping. Together with tracking my medication time in Google Forms, if I wonder if my medication is off or something.

Interviewer: What do you think are common problems with self-tracking apps that are on the market?

Riggare: They are not flexible enough, I can only measure exactly what someone else has decided. Besides, they are not holistic enough. They don’t take the full complexity of Parkinson’s into account. Next to that, not all apps are patient-centric enough. They are often focused on measures that healthcare thinks are important, rather than what people with Parkinson’s value. There is one app called mPower from Apple. You cannot use it if you’re not in the U.S. Their ethical approval only covers the US. I can understand, but I think it’s wrong that research goes before helping patients.

Interviewer: What did you think about the design and usability of mPower?

Riggare: I have some screenshots of the early design. The design is good, Apple is always good at design. In terms of usefulness, I don’t know. What I do know, is that they have not been able to give people back their data. So I will expect that won’t be useful.

Interviewer: Do other apps do give people back their data?

Riggare: If you pay for an app, you wouldn’t pay if you wouldn’t give you feedback. All other apps give you data back, but they are not as complex as mPower. It would be very

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interesting if I could have the data back. They use individual data to learn things on a group level, but to manage your own PD, you need to think on the individual level. If mPower is designed for group level results, maybe it’s not useful for individual level.

Interviewer: What do you think could be useful additions to current self-tracking apps, that could solve some of these problems?

Riggare: There are so many aspects of Parkinson’s as I’m sure you know. Blood pressure, heart rate, fatigue, constipation, cognitive problems, being out of breath, so many things that potentially could be related to PD. If you want to look at PD as a whole, you can put together lots of different tracking aspects, but for an individual probably only a few of them would be relevant. What would probably be relevant for everybody, would be some sort of medication tracking or timing. It still seems difficult to create a medication app that’s easy to use. You’d want to have a reminder of when you need to take your medication. But that timing can vary. You also want to say ‘I took it now’ or ‘I didn’t’. The thing that should be simple and easy, even that app doesn’t exist. I don’t say it is easy, but it is easier than to make a complete tracking app.

It would be interesting if you had an app where you can build your own tracking system of modules in a way. If such an app would exist, it would also cover multiple chronic diseases. Problem could be that it is too unspecific, that it would be difficult to say what it actually does.

Tracking takes time. Lots of people think that if you just give patients an app, they will track everything all the time. But they don’t want to track everything all the time. The problem is that most apps assume/are designed to track continuously, if not 24/7 so at least every day.

Interviewer: Do you have experience with tracking your exercise?

Riggare: Yes I do, I have an Apple Watch, on which I track numbers like my heart rate and stuff. However, if I want to put it together with my other data, there is a lot of work I have to do. It doesn’t do it automatically. Tracking exercise is definitely a good thing. If nothing else so, because by tracking you can learn if it actually helps you or not. This is something that research doesn’t even know yet, so by tracking exercise we can learn a lot about what works and what doesn’t work.

Interviewer: Do you feel self-tracking data can be used to improve people with Parkinson’s experience with their disease?

Riggare: I think it has the potential to. But for some, there needs to be more expertise at it. The new version of Apple Watch, version 4, has the complete ECG build in. If you don’t know how to interpret an ECG chart, that could be a problem. If you can’t understand the data, there’s not much you can do with it. But of course you can learn. As long as you have support and someone to discuss with, it could be very helpful.

Interviewer: And do you think it is useful for healthcare in general?

Riggare: Definitely. If both for them to learn about what works for individual patients and that they can test on other patients. Also for them to train how to be a coach for patients, rather then being a caregiver. Because of course this sort of shifts the power. Patients now can get their data and act on it. The role of healthcare needs to change.

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Interviewer: Do you ever wonder about the privacy aspects of gathering personal data when self-tracking?

Riggare: This is a question that has been discussed a lot in Sweden, probably by a lot of other countries as well, on policy level. I think most patients and people in general don’t know and they don’t care. But I think its important to get it right to start with. There can be a lot of problems downstream if you do it the wrong way. I am not too worried, but I can see the reasons why people could be worried, depending on what disease you have and what situation you have. Especially if you have an insurance system that is different from our Swedish system. So even if in the U.S. in some places, if data of your disease comes out and reaches your insurance company, it could be that your insurances costs increase. There are problems potentially in this, but personally I’m not too worried. That’s because I think I know enough about the risks to be aware of them. But I know this is an important issue.

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7.2 Interview with Mariëtte Robijn

Time: 09:00, 01-04-2019 Place: Amsterdam

Keywords: Parkinson’s Disease, self-tracking, apps, healthcare

Interviewer: Wanneer ben je begonnen met het tracken van symptomen en hoe deed je dat?

Robijn: Ik begon met tracken door een onderzoek van het Radboud UMC. Daar heb ik een app getest waarmee je elke dag 3 tot 6 verschillende dingen op meerdere tijdstippen moest checken. De app lijkt op de mPower app. Dit heb ik twee weken lang gedaan en toen had ik pas een jaar Parkinson, dus ik was nog enthousiast om mee te doen.

Interviewer: Wat vond je van de app van Radboud UMC?

Robijn: Die test was prima, hij testte allerlei motorische dingen, zoals lopen en steppen. Maar het was heel confronterend, want ik had nog helemaal geen last van die symptomen. De test is daarnaast ook na twee weken gestopt, dus ik heb niet door de maanden heen kunnen kijken hoe het met mezelf ging.

Interviewer: Zijn er nog meer manieren waarop je symptomen bijhoudt?

Robijn: Elke keer als we naar de neuroloog gaan, dat is vier keer per jaar, lopen we alle dingen langs die we kunnen bedenken en proberen we daarin te zien wat er beter gaat en wat er is veranderd. Dan proberen we daar het verband te zien tussen of je bijvoorbeeld vakantie hebt gehad of veel hebt gewerkt. Dat houden we zelf bij, maar de meetpunten zijn dus niet heel frequent.

Interviewer: Heb je ook ervaring met apps op je telefoon?

Robijn: Ja, ik heb wel eens de app van APDA gebruikt, deze is heel mooi en uitgebreid. Ik heb ook een app gebruikt voor het doen van de Stroop Test. Die gebruiken we bij boksen. Voor en na de training doen we deze test dan soms. We merken dat je er voor de training meer moeite mee hebt dan na de training.

Interviewer: Wat vond je van de APDA app?

Robijn: Die heb ik niet toegepast, alleen even bekeken. Vond het een hele mooie uitgebreide app. Wat ze goed toevoegen is ‘My questions for my doctor’. Dan kan je meteen bedenken wat je aan je neuroloog wilt vragen, want vaak vergeet je dat dingen te noemen. Je hebt alleen natuurlijk wel meer professionals die je af en toe ziet, zoals de fysio, verpleegkundige, dietiste of logopediste. Verder is de app makkelijk in gebruik. Het slaat je data op in een soort spinnenweb, waarin je kan zien wat zwaarder en minder zwaar wordt. Denk dat dit best ingewikkeld is voor veel mensen, maar voor mezelf zou het prima te gebruiken zijn.

Interviewer: Denk je dat de gezondheidszorg in Nederland baat kan hebben bij self-tracking systemen?

Robijn: In Nederland heb je heel veel therapeuten. In het algemeen kun je in Nederland vier keer per jaar naar je neuroloog. Daarnaast ga ik twee keer per jaar naar de Parkinson verpleegkunidge en dan heb ik één uur de tijd. Ik kan ook naar de fysiotherapeut, logopedist en ergotherapeut. Ik heb dus heel veel contactmomenten met een Parkinson professional per jaar. Als je dat vergelijkt met Amerika of Zweden, waar Sara Riggare woont, die mede met self tracking begon omdat ze het leuk en

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interessant vind, maar ook omdat ze maar 10 min per jaar bij specialist heeft. In Nederland is dat heel anders. Ik denk dat Nederland een heel goed verzorgd Parkinson land is. Je hebt mensen die het allemaal bijhouden, maar als er maar iets dwars zit kan ik altijd de verpleegkundige bellen. Wij hebben veel meer professionals om ons heen, dus ik denk dat self-tracking daarom misschien minder gebruikelijk is bij ons dan in andere landen. Andere mensen die in internationaal ken, mogen blij zijn dat ze eind volgende week naar de arts kunnen als er iets is. In Edinburgh zit bijvoorbeeld maar één Parkinson fysiotherapeut. Wij in Nederland zitten dus veel dichter bij de zorg. We hebben minder noodzaak om alles bij te houden.

Interviewer: Wat vind je handig aan huidige self-tracking systemen?

Robijn: APDA is erg duidelijk. Grote knoppen maken het veel makkelijker. Het moet heel simpel eruit zien, anders is de drempel te hoog om de app te gebruiken.

Interviewer: Waar loop je tegenaan bij huidige self-tracking systemen?

Robijn: In apps die nu bestaan noteer je vaak alleen actieve uren. Je meet bijvoorbeeld hoelang je hebt gesport, maar niet hoelang je op kantoor stil hebt gezeten. Het zou goed zijn om ook deze negatieve uren te meten, want deze kunnen een negatieve impact op je symptomen hebben.

Apps meten niet wat je wil meten, je kunt niet zelf instellen wat je wanneer wilt meten. Je wilt zelf kunnen instellen dat je bijvoorbeeld twee keer per week gaat boksen en dan kunnen bekijken of je daarna beter kan lopen/praten/fingertappen. Dit is vooral belangrijk voor mensen die ouder zijn en wat verder in Parkinson zitten. Zij moeten dan wel zelf moet kunnen inschatten wat ze meten en wanneer, maar dat is moeilijk. Stel je voor dat je veel last hebt van on en off periodes. Je wil een goed verhaal bij je dokter neerleggen, want straks zegt hij dat het veel erger met je is. Dan ga ik misschien alleen maar meten in mijn on periode. Een van de redenen dat niet alles wil bijhouden, is dat je kan zien dat het erger met je gaat, al is het meestal beter om iets te weten dan het niet te weten.

Interviewer: Wat zou je verbeteren of toevoegen aan een self-tracking systeem?

Robijn: Dat je een soort benchmark in een app hebt. Er bestaat geen benchmark van Parkinson, maar dat je je eigen benchmark gebruikt. Stel dat je 10 symptomen meet, die van invloed zijn op hoe je je voelt. Het lijkt me handig dat je dan zelf kan kiezen welke parameters je met elkaar wil vergelijken en dat je kan je eigen resultaten van terugzien en vergelijken met je benchmark. Een ander idee is dat je met 1000 mensen in de app zit en je een anonieme benchmark hebt. Dit kan wel voor stress zorgen, omdat je het eigenlijk allemaal niet wil weten. Misschien heb jij meer of minder last van symptomen en dan wordt je daar mee geconfronteerd. Hier kan je over nadenken in verder onderzoek.

Verder zou het leuk zijn om rewards te ontvangen als je iets goed doet, bijvoorbeeld als je hebt gesport. Dat je sterren of smileys krijgt. Komende generaties zijn daar aan gewend met games op hun telefoon en ik denk dat zij er gevoeliger voor zijn, een spelelement zou dan dus goed kunnen werken. Het zou ook interessant zijn om een community met puntensysteem te gebruiken in een app.

De Garmin Watch meet ook heel veel. Die meet ook je pas, hoeveel je voeten uit elkaar stappen. Dat wordt natuurlijk kleiner met Parkinson. Het zou dus mooi zijn als je dat kan koppelen aan andere parameters. Het is al bewezen dat sport goed is voor mensen

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met Parkinson en dat je je er beter door voelt. Daarom zou ik graag een app willen, waarmee je dat aan jezelf kunt bewijzen. De ultieme app neemt alle aspecten van Parkinson mee.

Interviewer: Maak je je zorgen over de data die self-tracking apps verzamelen over je?

Robijn: Je weet niet wat er met je data gebeurt. In principe wil ik alleen zelf mijn data zien. Ik wil niet dat het naar allerlei farmaceuten gaat. Aan de andere kant wel als het helpt met onderzoek doen, indien de gegevens anoniem en encrypted zijn. Dan zou het zo zijn dat ik bepaal wat ik meet en dat zij mogen kijken wat ze ermee doen. Als ze zouden sponsoren zouden mensen misschien wel sneller meewerken.

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7.3 Clickable Demo

Created with Marvelapp online via https://marvelapp.com/dashboard/. Link to project: https://marvelapp.com/5199356

1. Home screen. 2. Add new training. 3. Personal statistics.

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7.4 System Usability Scale Template

Table 5. Original questions and translated questions from SUS test.

Original question (English) Translated question (Dutch)

1. I think that I would like to use this system frequently.

1. Ik denk dat ik deze app vaak zou gebruiken.

2. I found the system unnecessarily complex. 2. Ik vond deze app onnodig complex . 3. I thought the system was easy to use. 3. Ik vond deze app makkelijk te gebruiken. 4. I think that I would need the support of a

technical person to be able to use this system.

4. Ik denk dat ik hulp nodig zou hebben om deze app te gebruiken.

5. I found the various functions in this system were well integrated.

5. Ik vind de verschillende functies in deze app goed geïntegreerd.

6. I thought there was too much inconsistency in this system.

6. Ik vind dat er te veel inconsistentie in deze app zit.

7. I would imagine that most people would learn to use this system very quickly.

7. Ik kan me voorstellen dat de meeste mensen deze app snel zouden leren gebruiken.

8. I found the system very cumbersome/awkward to use.

8. Ik vond deze app erg

omslachtig/ingewikkeld te gebruiken. 9. I felt very confident using the system. 9. Ik voelde me heel zelfverzekerd terwijl ik

deze app gebruikte. 10. I needed to learn a lot of things before I

could get going with this system.

10. Ik moest veel leren voordat ik deze app kon gebruiken.

How should a symptom self-tracking system for people with Parkinson’s Disease be designed for optimal usability?