Concerns about falling (CaF) are common in older people and have been associated with avoidance of activities of daily life. Exercise designed to prevent falls can reduce CaF, but the effects are usually short-lived. Cognitive behavioural therapy (CBT) can reduce CaF for longer but is not readily available in the community and unlikely to prevent falls. A multidomain intervention that combines CBT, motivational interviewing and exercise could be the long-term solution to treat CaF and reduce falls in older people with CaF. This paper describes the design of a randomised controlled trial to test the effectiveness of two different 12 week self-managed eHealth programmes to reduce CaF compared with an active control.

A total of 246 participants (82 per group) aged 65 and over, with substantial concerns about falls or balance will be recruited from the community. They will be randomised into: (1) myCompass-Own Your Balance (OYB) (online CBT programme) intervention or (2) myCompass-OYB plus StandingTall intervention (an eHealth balance exercise programme), both including motivational interviewing and online health education or (3) an active control group (online health education alone). The primary outcome is change in CaF over 12 months from baseline of both intervention groups compared with control. The secondary outcomes at 2, 6 and 12 months include balance confidence, physical activity, habitual daily activity, enjoyment of physical activity, social activity, exercise self-efficacy, rate of falls, falls health literacy, mood, psychological well-being, quality of life, exercise self-efficacy, programme adherence, healthcare use, user experience and attitudes towards the programme. An intention-to-treat analysis will be applied. The healthcare funder’s perspective will be adopted for the economic evaluation if appropriate.

Ethical approval was obtained from the South Eastern Sydney Local Health District Human Research Ethics Committee (2019/ETH12840). Results will be disseminated via peer-reviewed journals, local and international conferences, community events and media releases.

ACTRN12621000440820.

In a small percentage of patients, pulmonary nodules found on CT scans are early lung cancers. Lung cancer detected at an early stage has a much better prognosis. The British Thoracic Society guideline on managing pulmonary nodules recommends using multivariable malignancy risk prediction models to assist in management. While these guidelines seem to be effective in clinical practice, recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models.

This study is a prospective, observational multicentre study to assess the clinical utility of an AI-assisted CT-based lung cancer prediction tool (LCP) for managing incidental solid and part solid pulmonary nodule patients vs standard care. Two thousand patients will be recruited from 12 different UK hospitals. The primary outcome is the difference between standard care and LCP-guided care in terms of the rate of benign nodules and patients with cancer discharged straight after the assessment of the baseline CT scan. Secondary outcomes investigate adherence to clinical guidelines, other measures of changes to clinical management, patient outcomes and cost-effectiveness.

This study has been reviewed and given a favourable opinion by the South Central—Oxford C Research Ethics Committee in UK (REC reference number: 22/SC/0142).

Study results will be available publicly following peer-reviewed publication in open-access journals. A patient and public involvement group workshop is planned before the study results are available to discuss best methods to disseminate the results. Study results will also be fed back to participating organisations to inform training and procurement activities.

NCT05389774.