Emerging Issues in Mobile Data Capture Methods across Multiple Domains: Learning from the User Experience

Emerging Issues in Mobile Data Capture Methods across Multiple Domains: Learning from the User Experience

Jo Cranwell, Xu Sun, David Golightly, Genovefa Kefalidou, Benjamin Bedwell, Sarah Sharples
DOI: 10.4018/978-1-4666-8583-3.ch009
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Mobile device-based data capture studies have potential as contextual data collection methods to address the limitations of the traditional paper-based diary method. The ever-evolving computing power of mobile phones broadens the potential applications of such methods in novel and interesting ways. While there have been a number of studies that demonstrate the power of the mobile device-based diary approach, there is less known about participants' experience of such studies. This chapter presents five case studies to bring together user experiences of participating in mobile data capture studies and evaluates how this can be fed into the future study design.
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As well as being a ‘target’ of human-computer-interaction (HCI) research, mobile devices have a tremendous value as a ‘tool’ for HCI research (Carter & Mankoff, 2007; Klasnja et al., 2008; Klasnja, Consolvo & Pratt, 2011). Mobile devices support a whole range of rich data collection opportunities, be that through event and location logging, through response to mobile-based surveys, or through the capture of user-generated text, audio, images and video. More than ever, people are familiar with such devices and routinely use them in their everyday lives. Indeed, there are 1.75 billion mobile smart phones in use in the world (eMarketer, 2014), presenting the possibility for a research ‘app’ to be loaded directly on to peoples’ personal devices. The result is a potentially less obtrusive way to capture behaviour, attitudes and perceptions at the time and place where they are occurring.

The ability to collect data in the field has a number of advantages. At a general level, it should lead to more ecologically valid data (e.g. automatic data capture such as place and time) particularly for activities, such as travel behaviour or health behaviour, where there is a potential disconnect between stated preferences, intentions, and actual behaviour (Gardner, 2009; Lally & Gardner, 2013). Another advantage is that it allows prolonged data capture without the need for a researcher to be present. Not only does this increase the number of data points, it allows a researcher to see how patterns of behaviour change over time (Klasnja & Pratt, 2012; Shiffman, Stone, & Hufford, 2008). Finally, mobile devices support flexible data capture, often using a number of media such as text, audio and video, at the point when the behaviour in question occurs, and the later use of that data for reflection with an investigator. This type of diary study is referred to as an ‘elicitation study’ (Carter & Mankoff, 2005), in contrast to ‘feedback’ studies which use constrained, predefined questions asked either at certain intervals or during certain events.

Whilst there is guidance available on how to approach paper and technology-based diary and experience sampling studies (Bolger, Davis, & Rafeili, 2003; Consolvo & Walker, 2003; Shiffman et al., 2008), there is less known about the participant experience of mobile data capture and how that can be fed into the design of methodology. The work presented in this chapter highlights the value of different approaches in the domain of HCI by drawing upon five case studies: a) an experience sampling application to capture serendipitous information acquisition, b) an application to record travel motivations and track travel behaviours, c) an application to monitor the use of data sources on a mobile devices to understand users’ ‘contextual footprint’ (i.e. their pattern of data use over time), d) an experience sampling application to record personal experiences while navigating within a cultural site, and e) an ecological momentary assessment (EMA) application to deliver a self-control strengthening behavioural intervention and to measure study adherence. While each Case varies in domain and specific data collection approach, all have the same aim of using a mobile application to capture contextual, performance or adherence data for user requirements and subsequent technology development.

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