Towards improving emotion self-report collection using self-reflection

In an Experience Sampling Method (ESM) based emotion self-report collection study, engaging participants for a long period is challenging due to the repetitiveness of answering self-report probes. This often impacts the self-report collection as participants dropout in between or respond with arbitrary responses. Self-reflection (or commonly known as analyzing past activities to operate more efficiently in the future) has been effectively used to engage participants in logging physical, behavioral, or psychological data for Quantified Self (QS) studies. This motivates us to apply self-reflection to improve the emotion self-report collection procedure. We design, develop, and deploy a self-reflection interface and augment it with a smartphone keyboard-based emotion self-report collection application. The interface

Personal Informatics Tools Benefit from Combining Automatic and Manual Data Capture in the Long-Term

Harnessing the research opportunities provided by the large datasets generated by users of self-tracking technologies is a challenge for researchers of both human-computer interaction (HCI) and data science. While HCI is concerned with facilitating the insights gathered from data produced by self-tracking systems, data scientists rely on the quality of such data for training more accurate predictive models, which can sustain the flow of insightful data even after manual self-tracking is abandoned. In this position paper we consider the complementary roles that manual and automated data capture methods hold and argue that interdisciplinary collaborations are vital for advancing long-term self-tracking, the research and intervention opportunities that come with it, and provide a concrete example of where such collaborations would fit

Comparative Evaluation of Touch-Based Input Techniques for Experience Sampling on Smartwatches

Smartwatches are emerging as an increasingly popular platform for longitudinal in situ data collection with methods often referred to as experience sampling and ecological momentary assessment. Their small size challenges designers of relevant applications to ensure usability and a positive user experience. This paper investigates the usability of different input techniques for responding to in situ surveys administered on smartwatches. In this paper, we classify different input techniques that can support this task. Then, we report on two user studies that compared different input techniques and their suitability at two levels of user activity: while sitting and while walking. A pilot study (N = 18) examined numeric input with three input techniques that utilize common features of smartwatches with a touchscreen: Multi-Step Tapping, Bezel Rotation, and Swiping. The main study (N = 80) examined numeric input and list selection including in the comparison two more techniques: Long-List Tapping and Virtual Buttons to scroll through options. Overall, we found that whether users are seated or walking did not affect the speed or accuracy of input. Bezel rotation was the slowest input technique but also the most accurate. Swiping resulted in most errors. Long-List Tapping yielded the shortest reaction times. Future research should examine different form factors for the smartwatch and diverse usage contexts

End-User Development of Experience Sampling Smartphone Apps - Recommendations and Requirements

Professional programmers are significantly outnumbered by end-users of software, making it problematic to predict the diverse, dynamic needs of these users in advance. An end-user development (EUD) approach, supporting the creation and modification of software independent of professional developers, is one potential solution. EUD activities are applicable to the work practices of psychology researchers and clinicians, who increasingly rely on software for assessment of participants and patients, but must also depend on developers to realise their requirements. In practice, however, the adoption of EUD technology by these two end-user groups is contingent on various contextual factors that are not well understood. In this paper, we therefore establish recommendations for the design of EUD tools allowing non-programmers to develop apps to collect data from participants in their everyday lives, known as "experience sampling" apps. We first present interviews conducted with psychology researchers and practising clinicians on their current working practices and motivation to adopt EUD tools. We then describe our observation of a chronic disease management clinic. Finally, we describe three case studies of psychology researchers using our EUD tool Jeeves to undertake experience sampling studies, and synthesise recommendations and requirements for tools allowing the EUD of experience sampling apps

Comparative Evaluation of Touch-Based Input Techniques for Experience Sampling on Smartwatches

Smartwatches are emerging as an increasingly popular platform for longitudinal in situ data collection with methods often referred to as experience sampling and ecological momentary assessment. Their small size challenges designers of relevant applications to ensure usability and a positive user experience. This paper investigates the usability of different input techniques for responding to in situ surveys administered on smartwatches. In this paper, we classify different input techniques that can support this task. Then, we report on two user studies that compared different input techniques and their suitability at two levels of user activity: while sitting and while walking. A pilot study (N = 18) examined numeric input with three input techniques that utilize common features of smartwatches with a touchscreen: Multi-Step Tapping, Bezel Rotation, and Swiping. The main study (N = 80) examined numeric input and list selection including in the comparison two more techniques: Long-List Tapping and Virtual Buttons to scroll through options. Overall, we found that whether users are seated or walking did not affect the speed or accuracy of input. Bezel rotation was the slowest input technique but also the most accurate. Swiping resulted in most errors. Long-List Tapping yielded the shortest reaction times. Future research should examine different form factors for the smartwatch and diverse usage contexts

Permissible preference purification:on context-dependent choices and decisive welfare judgements in behavioural welfare economics

Behavioural welfare economics has lately been challenged on account of its use of the satisfaction of true preferences as a normative criterion. The critique contests what is taken to be an implicit assumption in the literature, namely that true preferences are context-independent. This assumption is considered not only unjustified in the behavioural welfare economics literature but unjustifiable–true preferences are argued to be, at least sometimes, context-dependent. This article explores the implications of this ‘critique of the inner rational agent’. I argue that the critique does not support a wholesale shift away from the use of true preferences as an evaluative standard in normative economics; instead, the critique implies that behavioural welfare economists need to inquire into and establish the ‘source’ of particular context-dependent choices in individuals’ decision-making. The source determines the permissibility of correcting individuals’ context-dependent choices and can, in some situations, support decisive welfare judgements.</p

Designing an experience sampling method for smartphone based emotion detection

Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus,