Features of interaction model

From the model of interactions in Figure 3.1, four explicit beneficial features of future smartphone ESM apps are derived, which could be incorporated into the design of tools for developing such apps, namely: implicit context triggering, explicit context triggering, two-way feedback, and preference tailoring. The extent to which these features are incorporated into an ESM app deployment is at the discretion of the researcher, directly influenced by their end-user development activities (Figure 3.1.H). The remainder of this section describes the benefits of such features in an EUD tool for ESM apps. Table 3.4 summarises how the features are related to sequences of the interactions previously described, with examples of their application. Further, these interaction sequences are illustrated as workflows extracted from Figure 3.1.

3.2.2.1 Implicit Context Triggering

Automated capture and inference of implicit sensor data (Figure 3.1.A), can support the delivery of assessments, interventions, or reminders at ideal times. Given the range of possible contexts mentioned in the literature, including location, activity, presence of others, noise, and mobile usage, it would be highly beneficial to allow researchers to easily specify the contextual conditions under which notifications should be sent. Further, implicit context could be used to deliver alerts to external contacts such as clinicians or carers. Figure 3.2 illustrates the workflow of such a

52 CHAPTER 3. SMARTPHONE DATA COLLECTION

Table 3.4:Potential features of ESM apps, and the relevant interactions from Figure 3.1

Benefit _InteractionsRelevant _ExplanationAdditional Implicit

Context Triggering

H→A→C

H→A→B

Researcher defines context and action (H) App detects trigger situation (A)

App prompts participant (C) or alerts the researcher/emergency contact (B)

Explicit Context Triggering

H→E→C

H→E→B

Researcher defines context and action (H) Participant inputs self-report (E)

App prompts participant (C) or alerts the researcher/emergency contact (B)

Two-Way Feedback

G→I→F

B→I→F

Participant issue is detected through

missing data (G) or direct alert from app (B) Researcher contacts participant to resolve issue (I) Participant responds (F)

D→F→I

C→F→I

Participant is concerned about their data (D) or receives intervention message (C)

Participant contacts researcher to resolve issue (F) Researcher responds (I)

Preference Tailoring G

→H→E

F→H→E

Issue arises through missing

data (G) or participant feedback (F)

Researcher adds tailoring function to app (H) Participant tailors app to resolve issue (E)

feature, wherein implicitly sensed contexts of interest are defined by a researcher (H) which, upon detection by the app (A) can prompt participants (C) or researchers themselves (B).

3.2.2.2 Explicit Context Triggering

A deliberate distinction has been made betweenimplicitandexplicitcontext. Implicit context refers specifically to the classification of implicit context such as location, activity, device usage etc. in order to trigger a relevant assessment or reminder. Explicit context, on the other hand, refers specifically to the ability for the device to perform functionality, such as giving relevant feedback to the participant, based on their explicit, self-reported information. The relationship is represented in Table 3.4, and illustrated in Figure 3.3, as E→C and E→B to represent how an

ESM app could trigger a response to a participant (C) or a researcher/clinician (B), following a self-report that satisfies the particular trigger conditions.

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Figure 3.3:A workflow representation of Explicit Context Triggering

Figure 3.4:A workflow representation of Two-Way Feedback

3.2.2.3 Two-Way Feedback

In clinical applications, automation is not a substitute for a patient-clinician relationship. Simi- larly, while less common in rigorous study protocols, researchers and participants could benefit from two-way communication to inform acceptable study designs and improve compliance. Table 3.4 briefly summarises examples of interaction sequences that could give rise to two-way feedback, initiated by either researchers or participants.

For example, if a researcher views a participant’s data and finds a discrepancy (G), or receives a direct alert via the ESM app (B), they can initiate a dialogue to ensure that the participant is coping with the study, represented as G→I→F and B→I→F respectively. This workflow is

also illustrated in Figure 3.4. Likewise, if the participant is concerned about their data (D), or receives an unusual intervention message in response to their self-report (C), they can contact the researcher to seek reassurance.

3.2.2.4 Preference Tailoring

A major problem in ESM studies, on paper, smartphones, or otherwise, is non-compliance and attrition resulting from the high burden of completing multiple surveys. Notification reminders can be sent to prompt compliance, but when these notifications are deemed intrusive in participants’ lives, this can aggravate the issue further. Participant attrition consequently causes burden for researchers, as the statistical power of study results is dependent on a larger sample

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Figure 3.5: A workflow representation of Preference Tailoring

size. Attrition could also be a particular concern in the medical domain, where patients’ health outcomes could be directly related to their compliance.

Hence, as expected, user-centred design studies report a need to tailor assessment timing and questions to participants’ personal characteristics. Again, Table 3.4 shows how this feature relates to possible interactions in the model. For example, a researcher may view participants’ data and find that assessments are not being completed at certain times or under particular conditions (G). They could resolve this by providing participants with the ability to tailor these times to their own preferences to maximise the chance of completion, represented by the G→H→E

interaction sequence. Likewise, a participant could proactively contact their researcher to request more flexibility in survey scheduling if they are struggling with inconvenient sampling times, represented by F→H→E. These interaction workflows are also illustrated in Figure 3.5.