This paper illustrates a rigorous approach to developing digital interventions using an evidence-, theory- and person-based approach. Intervention planning included a rapid scoping review that identified cancer survivors’ needs, including barriers and facilitators to intervention success. Review evidence ( N = 49 papers) informed the intervention’s Guiding Principles, theory-based behavioural analysis and logic model. The intervention was optimised based on feedback on a prototype intervention through interviews ( N = 96) with cancer survivors and focus groups with NHS staff and cancer charity workers ( N = 31). Interviews with cancer survivors highlighted barriers to engagement, such as concerns about physical activity worsening fatigue. Focus groups highlighted concerns about support appointment length and how to support distressed participants. Feedback informed intervention modifications, to maximise acceptability, feasibility and likelihood of behaviour change. Our systematic method for understanding user views enabled us to anticipate and address important barriers to engagement. This methodology may be useful to others developing digital interventions.

Carers -people who provide regular support for a friend or relative who could not manage without them -frequently report high levels of stress. Good emotional support could help relieve this stress. This study uses seven scenarios that depict different types of stress and acquires emotional support messages for them. We then categorize and evaluate the emotional support for different types of stress. We found that telling the carer they are appreciated and offering support are the best types of emotional support. Additionally, we found that how well a supporter sympathises with a situation affects the type of support they consider suitable. We describe and evaluate an algorithm that selects different categories of support to be used by an intelligent virtual agent to provide emotional support to carers experiencing different types of stress.

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A recent systematic review of randomised trials suggested that empathic communication improves patient health outcomes. However, the methods for training healthcare practitioners (medical professionals; HCPs) in empathy and the empathic behaviours demonstrated within the trials were heterogeneous, making the evidence difficult to implement in routine clinical practice. In this secondary analysis of seven trials in the review, we aimed to identify (1) the methods used to train HCPs, (2) the empathy behaviours they were trained to perform and (3) behaviour change techniques (BCTs) used to encourage the adoption of those behaviours. This detailed understanding of interventions is necessary to inform implementation in clinical practice. We conducted a content analysis of intervention descriptions, using an inductive approach to identify training methods and empathy behaviours and a deductive approach to describe the BCTs used. The most commonly used methods to train HCPs to enhance empathy were face-to-face training (n = 5), role-playing (n = 3) and videos (self or model; n = 3). Duration of training was varied, with both long and short training having high effect sizes. The most frequently targeted empathy behaviours were providing explanations of treatment (n = 5), providing non-specific empathic responses (e.g. expressing understanding) and displaying a friendly manner and using non-verbal behaviours (e.g. nodding, leaning forward, n = 4). The BCT most used to encourage HCPs to adopt empathy behaviours was “Instruction on how to perform behaviour” (e.g. a video demonstration, n = 5), followed by “Credible source” (e.g. delivered by a psychologist, n = 4) and “Behavioural practice” (n = 3 e.g. role-playing). We compared the effect sizes of studies but could not extrapolate meaningful conclusions due to high levels of variation in training methods, empathy skills and BCTs. Moreover, the methods used to train HCPs were often poorly described which limits study replication and clinical implementation. This analysis of empathy training can inform future research, intervention reporting standards and clinical practice.

Personality impacts all areas of our lives; it governs who we are and how we react to life’s challenges. Personalized systems that adapt to end users should take into account the user’s personality to perform well. Several methodologies (e.g. User-as-Wizard, indirect studies) that use personality adaptation require first for personality to be conveyed to the participant; this has few validated approaches. Furthermore, measuring personality is often time consuming, prone to response bias (e.g. using questionnaires) or data intensive (e.g. using behaviour or text mining). This paper presents a methodology for creating and validating stories to convey psychological traits and for using such stories with a personality slider scale to measure these traits. We present the validation of the scale and evaluate its reliability. To evidence the validity of the methodology, we outline studies where the stories and scale have been effectively applied (in recommender systems, intelligent tutoring systems, and persuasive systems).