VEREDA ÁGUILA MEDIA

2.4 Representation of Parkinson’s in the HCI Literature

The role of digital technology in healthcare has been widely explored since the introduction of organized telemedicine programmes in the late 1950s (Ryu, 2010). With recent growth in the ownership of mobile phones, personal computers and more recently smartphones and portable tablets (Ofcom, 2015), there has been a growing appreciation of the role that technologies can have in enhancing self monitoring and management practices. The

following sections provide an overview of how digital technologies are currently being used to support self monitoring and management of health and how Parkinson’s is positioned within the associated literature.

2.4.1 Tracking Health

As consumer technologies such as smartphones and tablets are becoming more widely available and affordable to the general public, we are seeing a rise in the appearance of health and wellness applications on mobile and wearable technologies. These devices now have the technical capabilities to collect, collate and make sense of a range of data

pertaining to health and fitness. Commercially available devices provide opportunities to capture, for example, movement data via built in accelerometers and gyroscopes, or heart rate using light sensors which monitor the blood flow through the veins in the wrist (e.g. the wrist worn Fitbit¹⁵ or Polar¹⁶ devices which track daily steps, count calories burned during exercise, monitor sleep patterns). Many of these devices communicate collected data to accompanying applications on mobile handsets, to aid those using them in the monitoring of their data both in-situ and over time.

As a result of the growing popularity of wearable health technologies and the increasing use of mobile technologies for health, the HCI research community has engaged widely in the ‘quantified-self movement’ (Choe, Lee, Lee, Pratt, & Kientz, 2014; Estrin, 2014). Work studying the design of personal informatics applications (Rooksby, Rost, Morrison, & Chalmers, 2014) has highlighted a range of self-tracking practices around the use of sensor and data logging platforms. In these instances, those using a range of data-logging and collecting technologies engage in practices of monitoring aspects of their own life, sometime through curiosity, sometimes to change certain behaviours or routines, or

15 http://www.fitbit.com/uk

16 http://www.polar.com/uk-en

25 sometimes to better personal performance in certain domains. A recent report from the Pew Research Centre (on 3,014 adults) noted that approximately 69% of all US adults tracked at least one health indicator for themselves or a loved one, and that those with a chronic health condition were more likely to track their health regularly (S. Fox & Duggan, 2013). It was also highlighted how the majority of people conducting health related self-tracking did so for their own or a family member’s benefit, and did not share this data with clinicians. Of the 69% adults engaging in tracking activities, 60% of these were tracking weight, diet or exercise routines.

Considering the rise in this quantified self movement and the generalised increase in visibility of monitoring or self tracking technologies—as well as increased familiarities

around the interpretation of personal data which is positioned alongside these technologies, It is perhaps unsurprising that self care has emerged as a particular area of interest.

However, there are certain intricacies that come into play when considering the self directed analysis and interpretation of specified health data (for example, lung function, blood

glucose levels, or motor ability) which might be the focus for self-monitoring.

Lupton describes the complexities which can arise when users of these types of self monitoring technologies are provided with data that suggests their “health is suffering, or if these data conflict with their own subjective and phenomenological interpretation of their state of health and wellbeing, this can be unsettling and anxiety – or fear—provoking”

(Lupton, 2013) (p.264). Particularly in the case of Parkinson’s, where symptoms can be transient throughout the daily drug cycle, and indeed the severity is expected to increase over time, this is a perspective which needs to be fully considered in the design of self monitoring and management systems, in order to understand the role that these types of technologies might play in supporting Parkinson’s. In a 2014 magazine article, Sara Riggare (Riggare, 2014), a researcher with Parkinson’s who blogs about her experiences with self tracking (www.riggare.se ) describes the practice as “the most powerful weapon I can wish for in my battle against Parkinson’s” (p.13). She describes how, in a typical year, she spends one hour with her neurologist receiving clinical guidelines about her condition, spending 8,765 hours engaging in self care. Riggare discusses tracking as a way to monitor and better understand her condition and her reactions to medication. In a later blog post however she calls to question the “burden of tracking”(Riggare, 2015), describing how continuous monitoring of her Parkinson’s takes time and planning, particularly when considering

26 complex medication routines and further practices such as regular exercise which help to manage motor symptoms. To borrow Lupton’s phrase, Riggare is very much a “digitally engaged patient” (Lupton, 2013). However, her unusually young age of diagnosis (32 years), her scientific background (engineer and self care researcher) and general interest in digital technology place her very much in the minority of ‘typical’ Parkinson’s patients. In this sense, her own experiences—as a digitally engaged patient—of ‘burden’ and ‘effort’ again highlight the importance of the careful design of self monitoring and management systems for Parkinson’s, to ensure that the technologies being developed can be seen as tools to support self care practices, as opposed to weighing patients down with information and laborious tasks.

This thesis aims to uncover these complexities in order to facilitate the design of digital systems which speak to the specificities of Parkinson’s and the needs and values that interplay to encourage the instigation and continuation of self care. The aim was to design technologies that would fit easily into the everyday lives of participants, without becoming a burden. There have been several previous attempts within HCI literature to explore self monitoring and management technologies for Parkinson’s, which will be described in the following section.

2.4.2 Self Monitoring and Management Technologies for Parkinson’s

The specific ways technologies might support Parkinson’s in self care practices remains relatively underexplored in the HCI literature. The vast majority of computing literature on Parkinson’s has focused on issues related to the assessment and diagnosis of motor aspects of the condition (Arora et al., 2015; Cai et al., 2014; T. Khan, Nyholm, Westin, & Dougherty, 2014; Martens et al., 2013; Westin, Dougherty, Nyholm, & Groth, 2010). For example, Westin et al. (Westin et al., 2010) describe the development of a touch screen based system to provide in-home motor testing on a range of condition relevant measures, including traditional finger tapping and spiral drawing tests as well as self-reported patient

information via a diary. In a recent piece of work, Arora et al (Arora et al., 2015) went a step further and described the development and evaluation of a smartphone application to assess a holistic battery of tests including voice, posture, gait, finger tapping and response time, all of which provide information towards the initial diagnosis of Parkinson’s and the disease state of a person already diagnosed with the condition at a given time. Arora et al.

27 were able to distinguish Parkinson’s patients from controls to a high degree, suggesting that their system could be effectively used in the remote assessment of Parkinson’s within rural and hard to reach areas.

A small amount of HCI research has explored the design of technologies for the self management of Parkinson’s. For example, de Barros et al. (de Barros et al., 2013) developed a suite of mobile applications to help PwP manage and track day-to-day aspects of their condition—such as ON/OFF fluctuations, medication reminders, and daily diary and data sharing features with clinicians. They found that, during the design phase, participants particularly valued having an ‘all in one’ solution to help them manage their Parkinson’s.

However the authors focused their evaluation on usability testing of their apps and did not reflect on the impact that their use had on participants’ lives. Moving more towards a rehabilitation technique, Mazilu et al (Mazilu et al., 2014), describe an in-situ prompting system for episodes of gait freezing. The system functioned using an ankle worn sensor that detected freezing episodes and provided an auditory metronomic prompt, on a mobile phone, to facilitate gait initiation and continuation. This method has similarly been used in a large clinical trial on 153 patients by Nieuwboer et al (Nieuwboer et al., 2007) using

temporal prompting (where prompts are simply programmed to go off within a specific time frame and do not rely on interpreting a data stream from the patient) with significant effect.

Indeed, gait in general is an element of Parkinson’s which has been widely explored, often through the use of accelerometers (inertia movement sensors) to help monitor and give situated support for gait management (e.g. (Bächlin et al., 2010; Casamassima, Ferrari, Milosevic, Rocchi, & Farella, 2013; Mazilu et al., 2014)). Often examples of such work rely on using machine learning techniques to detect episodes of freezing and providing ‘cues’ that aid PwP to continue their movement. Unlike Nieuwboer et al.’s work, Mazilu et al. (Mazilu et al., 2014) highlight the benefits PwP gain from in-situ cueing over continuous cueing, which can become habituated over time.

The examples of work described above add significant understanding towards how self monitoring and management of Parkinson’s is understood within the literature and provide a starting point for understanding the ways that technologies might be configured for Parkinson’s symptoms. However, they all focused their research questions around usability of the designed systems and ways in which objective clinical goals were met. There are still unanswered questions around how PwP might interpret and make sense of data

28 being provided by these types of technologies, and a lack of research focusing on obtaining experience rich qualitative accounts of the impact digital technologies might have on self monitoring and management practices in everyday life. In addition, there is currently very little knowledge about how the specific symptoms of speech and swallowing might be supported. The following sections provide an overview of how technology is being currently used within SLT and, specifically, SLT in Parkinson’s.

2.4.3 Technology for SLT

The use of digital technology in the field of SLT is longstanding. One of the key areas where technology has impacted on SLT has been in the development of Augmentative and

Alternative Communication (AAC) systems. AAC is a general term used to describe methods of aided communication. These can be through general non-verbal strategies such as gesture or body language, the use of picture books or communication charts, or through a range of different technologies which can act as a substitute vocal communication aid (Glennen &

DeCoste, 1997). The types of technologies used for AAC are diverse with varying

complexities—from equipment with simple text to speech functions, picture based ‘buttons’

that relay messages when pressed, to eye-gaze technology for those who are physically unable to physically interact with a system. However, these specialized technologies are often expensive and frequently require the attainment of external funding, or for individuals (or their families) to fund these privately in order to use them in the long term. It is

unsurprising, therefore, that there has been a recent rise in the development of AAC apps that can be simply purchased and downloaded from commercial app platforms and installed on personal tablets and mobile devices, which also brings advantages in terms of cost, updateability and ongoing sustained use.

However, the interest in mobile applications for SLT extends further beyond the AAC domain. Recent work has started to explore the role technology might play in supporting clinical practice within therapeutic contexts, with a wide range of research being conducted into the development of digital tools to support SLT. Many of these applications are aimed at children, often to enhance the enjoyment of speech therapy tasks through the gamification of tasks and to facilitate home practice (Bastanfard, Rezaei, Mottaghizadeh, & Fazel, 2010;

Fardoun, Kateb, & Paules Cipres, 2014; T. Lan, Aryal, Ahmed, Ballard, & Gutierrez-Osuna, 2014; Parnandi et al., 2013). However, there has also been work aimed at adults who are engaging in SLT programmes, mainly around aphasia—a communication impairment

29 affecting the expression of, or comprehension of, spoken and written language (Allen,

McGrenere, & Purves, 2008; Kuwabara, Shimode, & Miyamoto, 2010; Piper, Weibel, &

Hollan, 2011; Stapleton, Whiteside, Davies, Mott, & Vick, 2014; Williams, Moffatt, McCall, &

Findlater, 2015). For example, Piper et al (Piper et al., 2011) described the Write-N-Speak system, which builds upon traditional paper-based therapy techniques, employed by many SLTs, to deliver customizable and interactive paper-based resources such as worksheets, stickers and photographs which can be loaded with personally meaningful and useful content audio. Moving away from the specific clinical therapy sessions and into the everyday lives of the patients, Kane et al (Kane, Linam-Church, Althoff, & McCall, 2012) described TalkAbout, a context-aware system which allowed users to access relevant word lists dependent on their location and conversation partner. Within a similar area, Williams (Williams et al., 2015) explored the potential for providing in-situ support for the access of vocabulary during conversation, using head-mounted wearable technologies such as Google Glass and wrist mounted touchpads for easy navigation.

Specific to Parkinson’s, there is emerging research on the use of SLT apps to improve vocal loudness. Eglin¹⁷, for example, has developed a voice training application to treat PwP with volume problems, which includes a ‘feedback-meter’ to encourage reflection around self perceptions of volume (a well-know issue for PwP, as discussed above in section 2.2) (Parkinson’s UK, 2015b). Krause et al (Krause et al., 2013) also focused on providing a visual representation of achieving an adequately loud voice but in their case in a playful way. The authors explored the potential of a Microsoft Kinect-based game in facilitating the home practice of vocal loudness exercises, using gamified modifications on different practice tasks to encourage a level of enjoyment whilst reaching SLT goals. Their participants showed significant improvement when practising their loud voice with the game compared to the original volume they had during the calibration phase of the study. However, the authors suggest that there is much further work required to explore how these types of games might support longer term motivation towards home practice in the future. Although these studies highlight how digital technologies can be useful for supporting the practice of speech within people’s homes, there is still little known about the motivations that might drive PwP to engage in these types of systems over traditional SLT practice.

17 Roger Eglin’s work is yet unpublished but the application (speech tool) is available for PwP to download and use via the app store:

https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewSoftware?id=807115217&mt=8 .

30 The research described above demonstrates clear opportunities that technology could bring within the domain of SLT; however, much of the focus thus far has been around supporting the clinical practice of the SLT and in repurposing more contemporary consumer mobile technology platforms to perform the job of more expensive equipment, or indeed to remediate older paper-based activities and therapy tools. Within the context of Parkinson’s, interest around supporting home practice of vocal exercises is beginning to emerge.

However there is little understanding around how digital systems might be used to motivate and support self monitoring and managing practices, by encouraging PwP to engage with meaningful data to reflect upon their symptoms. The work in this thesis explores this

concept by covering a spectrum of technologies using different feedback mechanisms—from passive cueing, requiring a basic response to a prompt, to active interpretation of feedback, encouraging self directed changes to practice behaviours.