<?xml version='1.0' encoding='UTF-8'?><xml><records><record><source-app name="HighWire" version="7.x">Drupal-HighWire</source-app><ref-type name="Journal Article">17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Keast, Jacob</style></author><author><style face="normal" font="default" size="100%">Simpson, Glenn</style></author><author><style face="normal" font="default" size="100%">Smith, Lucy</style></author><author><style face="normal" font="default" size="100%">Dambha-Miller, Hajira</style></author></authors><secondary-authors></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Artificial intelligence-driven exercise programmes in personalising the management of multimorbidity</style></title><secondary-title><style face="normal" font="default" size="100%">BJGP Open</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2025-10-01 00:00:00</style></date></pub-dates></dates><elocation-id><style  face="normal" font="default" size="100%">BJGPO.2025.0094</style></elocation-id><doi><style  face="normal" font="default" size="100%">10.3399/BJGPO.2025.0094</style></doi><volume><style face="normal" font="default" size="100%">9</style></volume><issue><style face="normal" font="default" size="100%">3</style></issue><abstract><style  face="normal" font="default" size="100%">Multimorbidity, the presence of two or more chronic conditions, presents significant challenges in health care. Multimorbidity affects over 25% of UK adults and is a growing global challenge, contributing substantially to disability-adjusted life years (DALYs) and healthcare costs.1 Long-term conditions, which frequently co-occur, now account for over 70% of global DALYs, underscoring the urgency of scalable, cost-effective interventions.2 Individuals with multimorbidity often struggle with complex treatment regimens, multiple medications, and care plans tailored to each condition, leading to fragmented care that may not address their overall health needs.3 Exercise is an important component in the management of chronic conditions, although there is a significant challenge in designing personalised approaches to accommodate the unique combination of health conditions a patient faces. Limitations of standardised exercise referral schemes include limited adaptability to individual progress, low adherence rates, and a lack of contextual personalisation4,5 Artificial intelligence (AI)-driven exercise programmes offer a promising solution, providing tailored and adaptable plans that respond to the specific needs of populations affected by multimorbidity. AI coaching models have a well-established presence in the sports industry; examples include TrainerRoad and Tri-Dot, which optimise training plans with feedback and review from each session’s outcomes.6 These models, developed over a decade, offer high quality, data-driven solutions that improve performance and engagement. For chronic health conditions, AI can similarly optimise exercise plans based on patients’ needs. For example, individuals with diabetes benefit from a mixed training approach with a preference for high-intensity efforts that stimulate hypertrophy and improve muscle glucose uptake, enhancing insulin sensitivity. Exercise has also been shown to improve pulmonary function and exercise tolerance in individuals with chronic obstructive pulmonary disease (COPD).7 In people with osteoarthritis and type 2 diabetes, structured aerobic and resistance training is associated with reduced joint …</style></abstract></record></records></xml>