Atrial Fibrillation (AF) is the most common arrhythmia worldwide affecting an estimated 5% of people over the age of 65 and is a leading cause of stroke and heart failure. Identification of patients at risk allows preventative measures and treatment before these complications occur. Conventional risk prediction models are static, do not have flexibility to incorporate dynamic risk factors and possess only modest predictive value. Artificial intelligence and machine learning-powered health virtual twin technology offer transformative methods for risk prediction and guiding clinical decisions.

In this prospective observational study, 1200 patients will be recruited in two tertiary centres. Patients hospitalised with acute illnesses (sepsis, heart failure, respiratory failure, stroke or critical illness) and patients having undergone high-risk surgery (major vascular surgery, upper gastrointestinal surgery and emergency surgery) will be monitored with a patch-based remote wireless monitoring system for up to 14 days. Clinical and electrocardiographic data will be used for modelling the risk of new-onset AF. The primary outcome is episodes of AF >30 s and will be described as ratio of episodes/patient and as percentage of patients having episodes of AF. Secondary outcomes include 30-day and 90-day readmission rates and complications of AF.

The aim of this study is to generate data for the development and validation of health virtual twins predicting onset of AF in an at-risk population. The intelligent monitoring to predict atrial fibrillation (NOTE-AF) study is part of the TARGET project, a Horizon Europe funded programme which includes risk prediction, diagnosis and management of AF-related stroke (https://target-horizon.eu/).

The study has received approval by the Health Research Authority and the National Research Ethics Service (REC reference 24/NW/0170, IRAS project ID: 342528) in the UK and has been registered on clinicaltrials.gov (NCT06600620). Results will be disseminated as outlined in the TARGET protocol to communicate project ideas, activities and results to diverse audiences.

NCT06600620.

Chronic pelvic pain, defined as persistent pain in the structures of the pelvis, is a condition that significantly impacts the health-related quality of life (HRQoL) of up to one-third of people worldwide, with substantial associated costs to both the individual and healthcare system/s. The present trial aims to establish the efficacy of e-hypnotherapy over relaxation and waitlist controls on pain, HRQoL and biopsychosocial outcomes, and evaluate cost-effectiveness.

A parallel-group, investigator-blinded, randomised controlled trial will be conducted. Eligible participants will be randomly allocated to either a 7-week online personalised e-hypnotherapy programme (n=44), a 7-week online personalised relaxation control (n=44) or waitlist control (n=44). The primary outcome will be self-reported pain level, and secondary outcomes will include psychological distress, QoL, pain catastrophising, self-efficacy, central sensitisation, somatic symptoms, fatigue and sleep. Cost-effectiveness will also be examined. Longitudinal qualitative interviews will be conducted with participants in the e-hypnotherapy (n=20) and relaxation (n=20) groups to understand meaningful change and barriers/facilitators for ongoing use.

This protocol has received ethics approval in Australia from the Deakin University Research Ethics Committee (DUREC ref. 2024-080). Findings will be disseminated through peer-reviewed publications and presentations at national and international conferences related to chronic pelvic pain and mind–body interventions.

Australian New Zealand Clinical Trials Registry ACTRN12623000368639p.