Exploring the Use of the Behavior Change Technique Taxonomy and the Persuasive System Design Model in Defining Parent-Focused eHealth Interventions: Scoping Review (Preprint)

Abstract

BACKGROUND Intervention effectiveness research is dependent on the ability to accurately identify the active ingredients of the intervention but is frequently sub-optimal. The active ingredients in parent-focused eHealth (PFeHealth) include the persuasive technology elements provided by the technology along with behaviour change techniques inherent in the intervention. Two taxonomies have been used to describe the active ingredients of eHealth interventions along with their associated behaviour models. The Fogg Behaviour Model (FBM) and Persuasive System Design Model (PSDM) relate to persuasive technology; whilst the Capability-Opportunity-Motivation:Behaviour Model (COM:B) and the Behaviour Change Techniques Taxonomy (BCTTv1) describe active relate to behaviour change. OBJECTIVE The objective of this scoping review was to explore how the active ingredients in parent-focused eHealth are used and reported in publications. Understanding similarities and differences in how the two taxonomies define the active ingredients was a second objective. METHODS A scoping review of parent-focused eHealth publications was undertaken. Publications needed to describe interaction between the parent and the computer interface to ensure Persuasive Technology was included in the intervention. Data from the included publications were charted and analysed using deductive content analysis based on the FBM, PSDM, COM:B and BCTTv1. RESULTS The scoping review mapped 23 PFeHealth interventions described in 42 articles published between 2012 and 2018. A range of chronic childhood health and behaviour concerns were represented including medical, behavioural, and developmental issues. The most popular features included in the interventions were interactive questionnaires or charts followed by instructional videos. Just over half the interventions mentioned a behaviour change theory. Only two interventions referenced PSDM and only one intervention referred to COM:B and BCTTv1. The most frequent behaviour change techniques included learning a behavioural skill, practising it, and self-monitoring the behaviour and its outcome(s). The most frequently used persuasive techniques were tailoring the intervention towards family needs and interests, simplifying tasks and individualising the intervention. The two taxonomies coded different constructs and could not be merged. Of the 125 combined BCTs and PSDs that could have potentially been used, over 40% were not represented in the included publications. Supplementary material listing BCTs included in the intervention was only provided by one of the 42 included publications. Discrepancies between this list of BCTs and the scoping review analysis were noted. CONCLUSIONS Merging taxonomies related to behaviour change and persuasive technology may fail to capture the persuasive ingredients inherent in technology. This scoping review highlighted the need for supplementary material describing the active ingredients within a PFeHealth publication, and the benefit of using two taxonomies to capture both the behaviour change and persuasive technology ingredients that influence the experience and behaviours of parents using the technology. CLINICALTRIAL not applicable

The Intersection of Persuasive System Design and Personalization in Mobile Health: Statistical Evaluation

“…In particular, research on the impact of certain intervention features on user engagement is an important next step in the development of theory and evaluation to develop a science for user engagement [ 6 ]. Although the positive influence of persuasion on changing an individual’s attitude and behavior has been established [ 7 , 8 ], researchers have contended the need for personalized systems that address individual’s personalities to increase the effectiveness of these tools [ 9 , 10 ]. One-size-fits-all digital health technologies that target behavior change to improve the user’s health often fail because they do not target the psychological traits that drive an individual’s motivations and behaviors, partly because of the lack of guidance from intervention designers and data scientists with numerous options [ 11 ].…”