Eliciting Preferences for the Uptake of Smoking Cessation Apps: Discrete Choice Experiment.

Background: If the most evidence-based and effective smoking cessation apps are not selected by smokers wanting to quit, their potential to support cessation is limited.

Objective: This study sought to determine the attributes that influence smoking cessation app uptake and understand their relative importance to support future efforts to present evidence-based apps more effectively to maximize uptake.

Methods: Adult smokers from the United Kingdom were invited to participate in a discrete choice experiment.

Vaccination invitations sent by warm and competent medical professionals disclosing risks and benefits increase trust and booking intention and reduce inequalities between ethnic groups.

Objective: We aim to identify vaccination invitations that foster trust and improve vaccination uptake overall, especially among ethnic minority groups who are more at risk from coronavirus disease (COVID-19) and less likely to be vaccinated.

Method: In a preregistered 4 × 4 mixed-design experiment, we manipulated how much risk-benefit information the message included within-subjects and the message source between-subjects ( = 4,038 U.K.

User feedback on the NHS test & Trace Service during COVID-19: The use of machine learning to analyse free-text data from 37,914 England adults.

Objectives: The UK government's approach to the pandemic relies on a test, trace and isolate strategy, mainly implemented via the digital NHS Test & Trace Service. Feedback on user experience is central to the successful development of public-facing Services. As the situation dynamically changes and data accumulate, interpretation of feedback by humans becomes time-consuming and unreliable.

Applying machine-learning to rapidly analyze large qualitative text datasets to inform the COVID-19 pandemic response: comparing human and machine-assisted topic analysis techniques.

Introduction: Machine-assisted topic analysis (MATA) uses artificial intelligence methods to help qualitative researchers analyze large datasets. This is useful for researchers to rapidly update healthcare interventions during changing healthcare contexts, such as a pandemic. We examined the potential to support healthcare interventions by comparing MATA with "human-only" thematic analysis techniques on the same dataset (1,472 user responses from a COVID-19 behavioral intervention).

An approach for embedding behavioural science in antimicrobial resistance One Health research.

Antimicrobial Resistance (AMR) is a global health emergency that threatens modern medicine and incurs great cost to human health. The World Health Organization as part of a quadripartite joint initiative with the Food and Agriculture Organization of the United Nations, World Organisation for Animal Health, and United Nations Environment Programme, has recently published a One Health Priority Research Agenda for AMR. In this article we present a multidisciplinary approach, proposed by behavioural science experts, One Health experts and AMR experts to support the implementation of the Priority Research Agenda.