Physical activity improves physical and psychosocial outcomes in healthy children and in children with a range of chronic health conditions. Unfortunately, children with chronic health conditions have lower levels of physical activity compared to their healthy peers due to multiple restrictions in physical activities and therefore tend to have lower levels of physical activity compared with their peers. This paper describes the protocol for Move to Improve, a pragmatic trial of an individualised physical activity intervention for children with chronic health conditions.

Using the RE-AIM framework, this study aims to test the feasibility of Move to Improve, an 8-week hospital-based individualised physical activity intervention. We will recruit 100 children aged 5–17 years who are diagnosed with type 1 diabetes, cancer, postburn injuries and cerebral palsy to a single-arm, pragmatic feasibility trial. The primary outcomes (objective moderate to vigorous physical activity, quality of life and goal attainment) and secondary outcomes (including aerobic capacity, body composition, motor function, grip strength and psychosocial outcomes) will be assessed at baseline, post intervention and at 6-month and 12-month follow-ups. We will conduct semistructured interviews with participants and their primary caregiver at a 2-month follow-up to capture aspects of feasibility. Quantitative data will be reported descriptively, and qualitative data will be analysed using thematic analysis. Data gathered from this study will inform service decision-making and future trials.

The study has received ethics approval from the Government of Western Australia Child and Adolescent Health Service Human Research Ethics Committee (RGS6677). Findings of this research will be communicated to the public through peer-reviewed publications, conference presentations, reports, infographics and information sheets. Modifications to the protocol will be outlined in the trial registry and journal publications. Authorship will be in accordance with the International Committee of Medical Journal Editors.

Australian and New Zealand Clinical Trials Registry Number: ACTRN12624000836538.

Traumatic brain injury (TBI) remains a major public health concern in India, with high mortality and long-term disability. Existing prognostic models, mostly developed in high-income countries using traditional methods, lack generalisability to the Indian context and do not use the potential of machine learning or multicentric data. This study primarily aims to develop, compare and validate machine learning methods, including the traditional approach, to predict 30-day mortality and 6-month functional outcomes in patients with moderate or severe TBI. A secondary objective is to describe and compare admission characteristics and outcomes (at discharge, 3 months, 6 months and 1 year) in TBI patients in tertiary care settings using descriptive analyses.

Data from the neurotrauma registry at Jai Prakash Narayan Apex Trauma Centre, department of neurosurgery, All India Institute of Medical Sciences (AIIMS), New Delhi, including patients admitted between 23 March 2022 and 22 September 2024, will be used for model development and internal validation. For external validation, retrospectively collected data from the same centre (May 2010 to August 2013) and prospectively collected data from AIIMS Patna (1 June 2022 to 30 November 2024) and Rajiv Gandhi Government General Hospital, Madras Medical College (MMC), Chennai (1 May 2022 to 31 October 2024) will be included. Prediction models for 30-day mortality and 6-month functional outcomes will be developed using both machine learning and traditional statistical techniques. Model performance will be evaluated based on discrimination, calibration and clinical utility, with the latter assessed through decision curve analysis (DCA). An online risk calculator will be developed based on the best-performing model to estimate outcome probabilities along with 95% CIs.

The institutional Ethics Review Board of respective data collection centres, that is, AIIMS, New Delhi, AIIMS, Patna, and MMC, Chennai, approved the study. Findings will be published in peer-reviewed journals and disseminated at national and international conferences.

This study will develop and validate prognostic models using traditional and machine learning methods tailored to the Indian TBI context. Multicentric, prospectively collected data will enhance generalisability, while clinical utility will be evaluated through DCA. Adherence to Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis + Artificial Intelligence (TRIPOD+AI) guidelines ensures methodological transparency. With external validation, these models may improve clinical decision-making, resource planning and patient-family communication in diverse Indian healthcare settings.