Engagement in HCI

Doherty, Kevin E.; Doherty, Gavin

doi:10.1145/3234149

Cited by 159 publications

(77 citation statements)

References 244 publications

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“…Looking at the various definitions (Appendix 1), engagement seems to be a much-debated concept in this field, and there seems to be no accepted definition. The two most recent studies both strive to tackle this issue using a systematic review but arriving a two seemingly different conclusions: whereas Perski et al created a new definition for their specific target area (digital behavior change interventions) (Perski et al, 2017), Doherty and Doherty stated that the field needs to move away from identifying one definition of engagement and that it is more important to select the most useful interpretation and measurement of engagement, based on the context (Doherty and Doherty, 2018).…”

Section: Digital Engagementmentioning

confidence: 99%

“…However, the majority of articles included in that review only viewed engagement in behavioral terms, that is, as usage. This call to see and measure engagement not (just) through usage data is shared by more researchers (Yardley et al, 2016;Short et al, 2018) not only within the field of eHealth technologies (O'Brien and Toms, 2008;Doherty and Doherty, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…In other words, the design of a technologically driven intervention strongly influences how it is perceived, how it is employed, and how effective it will eventually be (Ludden et al, 2015;Kelders, 2019). Given the importance that the design of a technological solution poses for both engagement and adherence, it is not surprising that it has become a centrally debated topic within the domains of Interaction Design and Human Computer Interaction in recent years (Doherty and Doherty, 2018). Researchers from these fields actively advocate for the design of highly engaging and immersive user experiences in order to enhance utilization and manage non-adherence.…”

Section: Introductionmentioning

confidence: 99%

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The Concept and Components of Engagement in Different Domains Applied to eHealth: A Systematic Scoping Review

2020

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Within the context of eHealth interventions, a shared understanding of what constitutes engagement in and with eHealth technologies is missing. A clearer understanding of engagement could provide a valuable starting point for guidelines relating to the design and development of eHealth technologies. Given the cross-disciplinary use of the term "engagement," investigating how engagement (and its components) is conceptualized in different domains could lead to determining common components that are deemed important for eHealth technological design. As such, the aim of this paper was 3-fold: (a) to investigate in which domains engagement features, (b) to determine what constitutes engagement in these different domains, and (c) to determine whether there are any common components that seem to be important. A comprehensive systematic scoping review of the existing literature was conducted in order to identify the domains in which engagement is used, to extract the associated definitions of engagement, and to identify the dimensionality or components thereof. A search of five bibliographic databases yielded 1,231 unique records. All titles, abstracts, and full texts were screened based on specific inclusion and exclusion criteria. This led to 69 articles being included for further analyses. The results showed that engagement is used in seven functional domains, categorized as follows: student (n = 18), customer (n = 12), health (n = 11), society (n = 10), work (n = 9), digital (n = 8), and transdisciplinary (n = 1) domains. It seems that some domains are more mature regarding their conceptualization and theorizing on engagement than others. Further, engagement was found to be predominantly conceptualized as a multidimensional construct with three common components (behavior, cognition, and affective) shared between domains. Although engagement is prolifically used in different disciplines, it is evident that little shared consensus as to its conceptualization within and between domains exists. Despite this, engagement is foremost seen as a state of being engaged in/with something, which is part of, but should not be confused with, the process of engagement. Behavior, cognition, and affect are important components of engagement and should be specified for each new context.

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Section: Digital Engagementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Concept and Components of Engagement in Different Domains Applied to eHealth: A Systematic Scoping Review

2020

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“…This raises the question of whether acceptance is motivated by the same factors and in the same manner, regardless of how long the system has been used. As stated in a review on user engagement, describing a concept as a process, rather than a discrete state, “enables the analysis of change over time” [ 35 ]. In line with this, some authors propose models that incorporate temporality.…”

Section: Introductionmentioning

confidence: 99%

Technology Acceptance in Mobile Health: Scoping Review of Definitions, Models, and Measurement

Nadal¹,

Sas²,

Doherty³

2020

J Med Internet Res

Self Cite

182

132

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Background Designing technologies that users will be interested in, start using, and keep using has long been a challenge. In the health domain, the question of technology acceptance is even more important, as the possible intrusiveness of technologies could lead to patients refusing to even try them. Developers and researchers must address this question not only in the design and evaluation of new health care technologies but also across the different stages of the user’s journey. Although a range of definitions for these stages exists, many researchers conflate related terms, and the field would benefit from a coherent set of definitions and associated measurement approaches. Objective This review aims to explore how technology acceptance is interpreted and measured in mobile health (mHealth) literature. We seek to compare the treatment of acceptance in mHealth research with existing definitions and models, identify potential gaps, and contribute to the clarification of the process of technology acceptance. Methods We searched the PubMed database for publications indexed under the Medical Subject Headings terms “Patient Acceptance of Health Care” and “Mobile Applications.” We included publications that (1) contained at least one of the terms “acceptability,” “acceptance,” “adoption,” “accept,” or “adopt”; and (2) defined the term. The final corpus included 68 relevant studies. Results Several interpretations are associated with technology acceptance, few consistent with existing definitions. Although the literature has influenced the interpretation of the concept, usage is not homogeneous, and models are not adapted to populations with particular needs. The prevalence of measurement by custom surveys suggests a lack of standardized measurement tools. Conclusions Definitions from the literature were published separately, which may contribute to inconsistent usage. A definition framework would bring coherence to the reporting of results, facilitating the replication and comparison of studies. We propose the Technology Acceptance Lifecycle, consolidating existing definitions, articulating the different stages of technology acceptance, and providing an explicit terminology. Our findings illustrate the need for a common definition and measurement framework and the importance of viewing technology acceptance as a staged process, with adapted measurement methods for each stage.

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“…As the intention of HRI is to engage human participants in interactions, detecting and measuring initial and ongoing engagement in HRI is critical for initiating and maintaining interaction [23,25,57]. Many approaches for measuring engagement rely on rich sensors, such as cameras, lasers, sonar, and audio sensors, and on sophisticated facial expression, gesture, gaze, body movements, physiological signals, and speech recognition technologies [1,44,73].…”

Section: Engagement Estimation In Hrimentioning

confidence: 99%

Learning to Engage with Interactive Systems

Meng

Lin

Francey

et al. 2020

J. Hum.-Robot Interact.

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Physical agents that can autonomously generate engaging, lifelike behavior will lead to more responsive and user-friendly robots and other autonomous systems. Although many advances have been made for one-toone interactions in well-controlled settings, physical agents should be capable of interacting with humans in natural settings, including group interaction. To generate engaging behaviors, the autonomous system must first be able to estimate its human partners' engagement level. In this article, we propose an approach for estimating engagement during group interaction by simultaneously taking into account active and passive interaction, and use the measure as the reward signal within a reinforcement learning framework to learn engaging interactive behaviors. The proposed approach is implemented in an interactive sculptural system in a museum setting. We compare the learning system to a baseline using pre-scripted interactive behaviors. Analysis based on sensory data and survey data shows that adaptable behaviors within an expert-designed action space can achieve higher engagement and likeability.

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Engagement in HCI

Cited by 159 publications

References 244 publications

The Concept and Components of Engagement in Different Domains Applied to eHealth: A Systematic Scoping Review

The Concept and Components of Engagement in Different Domains Applied to eHealth: A Systematic Scoping Review

Technology Acceptance in Mobile Health: Scoping Review of Definitions, Models, and Measurement

Learning to Engage with Interactive Systems

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