A model of interactions

The interactions of researchers shape the course of their specific smartphone ESM study. Moreover, they constitute a model of interactions between participants, researchers, apps and data, as shown in Figure 3.1. In this model, the researcher’s interactions are represented by arrows G, H and I, and participants’ interactions by arrows D, E and F. At the centre of this model is the ESM app, which mediates the various interactions between researchers, participants and their data, and also has its own interactions with these three entities, represented by arrows A, B and C. These interactions are described in detail, with implications for smartphone ESM.

3.2.1.1 ESM App Interactions

An ESM app is more than a static diary, and can autonomously initiate interactions with the participant through prompting of assessments. However, ESM apps can also interact with a researcher through wireless connectivity, and with a participant’s data through on-board sensor classifiers. These interactions will be explained as follows.

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Figure 3.1:A model of interactions between researchers, participants, and ESM apps

Interaction A - Receiving and automatically classifying data

Self-reports can assess intentions, attitudes or certain explicit physical symptoms (such as discomfort or pain). However, an increasing breadth of information can be implicitly assessed from sensors built into the smartphone, or externally worn. In doing so, passively collected sensor data can minimise self-report burden, provide researchers with richer information, and enable tailored assessment and intervention strategies.

In psychology research, the UBhave project is investigating how, in combining implicitly sensed data and explicit self-report, apps can learn and classify sensor readings that relate to particular behaviours, such that interventions could be automatically triggered at appropriate times [86]. With the burgeoning interest in machine learning, the sophistication of classifications will continue to grow. Google has available Android APIs for accessing its complex, resource- efficient classifiers. Further, other high-level contexts such as the presence of others, level of sociability, quality of sleep, or susceptibility to interruption have been inferred from sensor classifiers instrumented in the computer science literature. For example, Ben-Zeev et al. showed that smartphone-sensed geospatial activity and sleep duration were significantly related to stress levels in a cohort of young people [87]. Adams et al. review smartphone sensing for monitoring physiological and behavioural biomarkers, which is a comprehensive source of further examples [7].

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Interaction B - Sending alerts to researcher

From knowledge acquired through explicit and implicit data, ESM apps in healthcare could inform clinicians of in-the-moment assessment issues, or emergency situations. In many conditions, early detection of symptomatic signs can prevent fatal consequences, or prevent relapse in psychological disorders, through clinician intervention.

Given that social psychology research often involves sensitive participants with learning difficulties or psychological disorders, it is ethically important to ensure that these participants have a means to request support. For example, the potential for apps carried by dependent individuals to send alerts to carers in critical situations could assist in the independent living of these people.

In a user-centred study on app requirements, clinicians and cancer patients both supported a feature to alert clinicians when patients explicitly reported high levels of pain [88]. Clinician alerts were also implemented in a randomised control trial, where physiological readings transferred to smartphones via Bluetooth, combined with explicit symptom reports, alerted clinicians to heart failure pre-conditions [89]. The trial reported improved health outcomes, with patients expressing feelings of reassurance and self-efficacy, and clinicians confirming the utility of receiving such alerts.

Interaction C - Sending notifications to participant

As described in Chapter 2, ESM sampling schedules can be signal-contingent, interval contingent, or event-contingent [47]. In addition, sensor data classifications provide implicit context, such that context-contingentsampling can be employed. Assessments could also be triggered on explicit context - if participants give certain responses it could be indicative of an interesting situation, which further assessment would then acquire the details of.

In considering conditions under which to send notifications to participants, it is also important to consider the conditions under which todefernotifications. Participant burden continues to limit the utility of ESM, where continuous prompting in everyday life can reduce compliance when these prompts occur at inopportune moments [12]. Sending notifications to participants to complete surveys is a core component of ESM apps in research. In smartphone-based implementations, three important types of notification have been identified, namelyassessments, interventions, andreminders, examples of which are shown in Table 3.3. Triggers for these prompts are further divided into implicit context and explicit context, further examples of which were described in Section 3.1.2.

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Table 3.3:Types of notifications, their contextual triggers, and examples

Communication Trigger Example

Assessment Implicit User is detected to have engaged in high levels of activity -send assessment to ask about exercise behaviour Explicit User self-reports abnormally high glucose reading -

send follow-up assessment to gather more information

Intervention Implicit User is near location of regular smoking behaviour -send user motivational message Explicit User self-reports high levels of distress in assessment -

send user support information

Reminder Implicit User has arrived home - send a reminder to take medication

Explicit User has not completed required number of assessments - send a reminder to complete those missed

Assessment

Explicit context refers not just to the answers given in completed assessments, but also those that were missed. Based on participants’ non-responses, further assessments can be sent. Particularly in psychology, researchers rely on continued compliance with data collection, such that the ability to send reminder prompts when self-reports have not been completed is a useful feature, and commonly applied in bespoke ESM apps. In discussing an app for obesity management, clinicians recognised the utility of such a function:

“Maybe the app could provide reinforcement messages that popup, like, ‘you tracked for 5 days in a row!’, ‘We haven’t heard from you in a while, let’s check in?”’[90, p. 812]

Given the need for consistent completion of assessments, inferring theinterruptibilityof ESM app users through passive data collection is thus an ongoing research effort. In the first study assessing mobile interruptibility, an externally worn triaxial accelerometer was used to prompt self-reports following a sudden drop or increase in movement, revealing increased receptivity in these states [84]. In the clinical domain, Sarker et al. collected data and inferred higher level information from a variety of sensors, correlating it with response rate to ESM surveys, to derive a prediction model of interruptibility with 77.9% accuracy, showing how implicit data can also be used to maximise ESM engagement [91].

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Interventions

Interventions can be dynamically delivered at locations of interest or on physiological measures. For example, the Q Sense app deployed tailored interventions when participants dwelt in identified smoking locations, with post-study feedback exhibiting positive response towards location triggers, and tailored messages [78].

One study combined results from implicit context classification, and explicit self-report to deliver an EMI to participants with clinical depression [92]. Participants received both mood state predictions, and motivational messages when self-reported mood fell below a threshold. Both features were positively received and raised self-awareness.

Reminders

A study of design recommendations for a cystic fibrosis app had participants suggesting that medication reminders be sent in response to self-report of administration [93]. A similar function where patients would receive notifications based on explicit medication reporting was suggested by a healthcare provider in a study by Simons et al.:

“I think it would be really useful if somewhere in the app, say when they’re...near the end [they receive a message saying] ‘You need to put in a request for repeat prescription’ ”[94, p. e31] For self-management, users with chronic conditions valued the possibility of context-sensitive reminders, such as those triggered at a particular location [93, 95]. One study prompted participants to describe their physical activity after levels of intense activity, or extended non- activity, were detected with a smartphone accelerometer [96]. Only prompting assessment when necessary reduces burden on participants. Moreover, from a healthcare perspective, this increased self-awareness could promote positive behaviour [34].

3.2.1.2 Participant Interactions

The very act of self-reporting may increase awareness of behaviours and contextual influences which, in ESM for research purposes, is undesirable given its effect on data validity. In clinical practice, however, raised self-awareness can improve health outcomes by giving patients a sense of independence and knowledge about their conditions. As well as simply having participants complete self-report assessments, smartphone ESM affords new possibilities for participant interaction, that could incite behaviour change, or simply improve study compliance.

Interaction D - Viewing aggregated, collected data

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enable rich visual charts of historical data to be displayed. Research suggests that visual feedback could raise engagement in patients with chronic health conditions, as well as compliance in research participants.

Participants trialling theMONARCAsystem valued both the ability to correlate their moods with implicit data, and to determine the temporal antecedents of low moods [97]. Remote monitoring technology was also welcomed by users with ADHD as a means to track symptoms over time through visual graphs, and identify unusual discrepancies [94]. Users with chronic conditions express interest in viewing their passively sensed information, particularly in the form of visual graphs, supported by participants with mixed chronic conditions [98], diabetes [99], and even serious mental illness [100].

From a research perspective, allowing participants to access visualisations or other means of interpreting their own data could improve compliance. A study by Hsieh et al. showed that providing participants with graphical summaries of their data increased compliance by 23% [58].

Interaction E - Tailoring app to personal preferences

Despite the motivational influence of empowerment to self-manage, maintaining a high level of engagement with apps is still a significant challenge. mHealth apps are easily removed or forgotten about, and patients thus must perceive sufficient value from use if they are to retain engagement. Indeed, recent statistics illustrate that a quarter of apps downloaded are only used once1_{. A balance must be sought between ensuring that app content is based on the input of a}

professional, while also taking into account the preferences of its end-users. The same appears to be true of psychology ESM apps - when participants are“researched objects”, isolated from the researcher and simply expected to comply with rigorous study schedules, this could induce non-compliance [101], and thus undermine the effectiveness of the ESM method.

User-centred design studies of health apps have elicited the importance of flexible prompt schedules to end-users [95, 102]. Moreover, end-users also desire to control the content of feedback that they are provided with [97, 98], as well as the format in which this feedback is presented [94]. For example, participants in a study on health data preferences by Miyamoto et al., although some participants wanted comprehensive information on blood pressure, sleep, and mood, others wanted minimal information such as caloric intake [98]. Similarly, in a study by Simons et al. on technology for monitoring ADHD symptoms, participants desired the presentation of both information input and output to be tailored to their preferences [94]. In research-based ESM applications, many studies begin by manually pre-programming mobile-

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based study applications to participants’ sleeping schedules. However, these schedules are subject to change on a daily basis, and flexible smartphone apps could provide participants the ability to adjust these preferences. For example, Markopoulos et al. showed that participants who could choose the time window in which they received assessments showed a significantly higher response rate than participants who received the questions on preset time slots [103]. In their study into human factors of smartphone surveys, Vhaduri and Poellebauer also proposed the use of personalised survey schedules to increase compliance in participants [104].

Interaction F - Providing feedback to researcher

While visual feedback and personalisation are both useful features for improving compliance and engagement, certain issues relevant to study app use may require a direct channel of communication from participant to researcher.

In the medical domain,“healthcare partnership”was an emergent theme in one focus group study [98]. Although context-augmented feedback can support patient self-management, focus group participants expressed a desire to contact clinicians for additional explanation of their received feedback. Involving clinicians in this“sensemaking”process was proposed to support participants in attaining maximum benefit from ESM. Additionally, individuals with cystic fibrosis identified how self-monitored data would enable them to provide clinicians with feedback between appointments [93].

Research investigating incentive mechanisms for participatory sensing suggests that participants would value being able to provide researchers with feedback. Anawar et al. found that a “participant feedback” incentive mechanic was a prevalent feature of popular weight loss participatory sensing apps [70]. In their participatory design process, Ludwig et al. found that it was important that participants feel like direct contributors to a study. Moreover, this feedback is beneficial from the researchers’ perspective - employing participatory design as part of an ESM deployment could enable researchers to address issues that could reduce the effectiveness of the data collection, through direct participant feedback [105].

3.2.1.3 Researchers’ roles as developers

Prior to smartphones, the researcher’s role in an ESM study was largely passive, and would typically involve installing an application, developed by a professional programmer, onto bespoke devices. These would then be distributed to participants, and collected following the study period for data download and analysis. In contrast, with instant data synchronisation possibilities, researchers can take a more active role in studies, by viewing real-time data and contacting participants if non-compliance is observed. Clinicians could play a similarly active role in

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mHealth ESM apps, with the ability to monitor patient data and provide feedback. Most importantly, in the context of this thesis, is the potential for researchers to independently create and deploy a study app, and modify it in real-time based on feedback received from participants or from their data.

Researchers could thus act as developers of smartphone-based ESM studies, creating and modifying apps to meet their diverse requirements. Smartphone technology would also afford further activities that support the researcher in this process. A description of researchers’ potential activities follows, benefits of which are supported by evidence from literature.

Interaction G - Viewing participant data in real-time

Researchers and clinicians could benefit from viewing incoming participant data during a study, or in between clinical appointments, affording their ability to react to potential issues as they occur. Data analysis could also be begun prior to study completion, in order to inform more useful, effective post-study interviews. For example, in their observations of researchers conducting diary studies, Carter et al. explained:

“Experimenters did not have time to review captured data before elicitation interviews, which curtailed their ability to prepare for the interview and increased the chance that important themes would be missed”[106, p. 126]

In clinical practice, instant access to self-monitored patient data could guide treatment decisions in face-to-face clinical appointments. Study participants living with cystic fibrosis [93] and ADHD [94] both strongly supported the provision of contextual information to clinicians. In an evaluation study of an app where such information access was implemented, paediatricians described how this saved time, focused appointments, and facilitated communication about difficult issues during these appointments [107].

Interaction H - Updating study protocol in real-time

Studies that cite the benefits of smartphone-based ESM all describe bespoke apps developed for the specific purpose of that study. Hence it appears that, although these benefits are generic and adaptable, their implementations are not. Thus, the researcher must be able to adapt these features found in bespoke apps to any ESM study they choose to run. In doing so, they perform end-user development (EUD) activities. (The field of end-user development is discussed further in Section 4.1.)

While theoretical models of behaviour can serve as guidance frameworks for app design, such theories do not capitalise on the full functionality of smartphone devices [108], nor do they

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account for the variation in requirements of different participant groups. Thus, the involvement of clinicians and patients in the design and evaluation of self-monitoring apps is important for ensuring their sustained use and utility [109]. Allowing researchers to create and modify their own ESM apps is central to the research questions of this thesis; it is hypothesised that, without EUD, the many potential benefits of smartphone ESM are constrained by the availability of a professional programmer to implement a bespoke application. Following such an implementation, any required modifications, however large or small, must be performed by the programmer. The significance of EUD is that the beneficial features of smartphone ESM are put directly into the hands of the researchers. Participants’ data and feedback can be directly acted upon, shaping apps in response to their dynamic and diverse requirements.

Interaction I - Providing feedback to participant

Feedback to participants could take a variety of forms, including lifestyle suggestions in clinical practice, or simply requests for compliance in a research study. In either case, there is support for provision of feedback through an ESM app. From their observation that researchers only downloaded data at the end of the study, Carter et al. noted an additional problem of this pattern: “Some of the media captured by users did not align with the goals of the study, and in one case

experimenters did not address this until the experiment ended. Being able to view user captures in real time could help with feedback”[106, p. 126]

From the participatory sensing literature, feedback from researchers could act as a non-monetary incentive mechanism, motivating them as active contributors to a study. A participant in the user-centred design study of Ludwig et al. explained this as follows:

“...one has to give the user something in return. So that they know that there has been progress and that based on that, new goals can be developed collaboratively with the user”[101, p. 498] In user-centred design studies of healthcare apps, feedback provision from clinicians to patients was also extensively discussed. Given that clinicians have little time outside of scheduled ap- pointments, there was unexpected enthusiasm for this type of interaction. It has been recognised