Acceptability of digital health interventions: embracing the complexity

Abstract

Acceptability is a core concept in digital health. Available frameworks have not clearly articulated why and how researchers, practitioners and policy makers may wish to study the concept of acceptability. Here, we aim to discuss (i) the ways in which acceptability might differ from closely related concepts, including user engagement; (ii) the utility of the concept of acceptability in digital health research and practice; (iii) social and cultural norms that influence acceptability; and (iv) pragmatic means of measuring acceptability, within and beyond the research process. Our intention is not to offer solutions to these open questions but to initiate a debate within the digital health community. We conducted a narrative review of theoretical and empirical examples from the literature. First, we argue that acceptability may usefully be considered an emergent property of a complex, adaptive system of interacting components (e.g., affective attitude, beliefs), which in turn influences (and is influenced by) user engagement. Second, acceptability is important due to its ability to predict and explain key outcomes of interest, including user engagement and intervention effectiveness. Third, precisely what people find acceptable is deeply contextualized and interlinked with prevailing social and cultural norms. Understanding and designing for such norms (e.g., through drawing on principles of user centered design) is therefore key. Finally, there is a lack of standard acceptability measures and thresholds. Star ratings coupled with free-text responses may provide a pragmatic means of capturing acceptability. Acceptability is a multifaceted concept, which may usefully be studied with a complexity science lens.

Keywords: Acceptability; Complexity; Engagement; Technology acceptance; eHealth; mHealth.

Fig 1

Suggested dynamic model linking the concepts of acceptability (an emergent property of a complex, adaptive system of interacting components), user engagement and intervention effectiveness. Inspired by the theoretical and conceptual frameworks by Sekhon et al. [5], and Perski et al. [6], in addition to the graphical representation of interaction-dominant systems by Hilpert and Marchand [7]. The transparent ovals represent a non-exhaustive network (a set of “best bets”) of interacting components. The dotted arrows leading to the grey ovals represent higher-level, emergent properties, arising from the interaction of components at a lower level. The thick, solid arrows indicate influences.

Fig 2

Example rating and free-text comment in ExerciseGuide [53].