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Diabetes Management and Research · Journal article
Current Issues in Sport Science · September 3, 2026
A consensus or society position rather than new primary data.
This keynote presentation synthesizes current evidence and expert consensus on the role of digital health applications, continuous glucose monitoring, automated insulin delivery, and artificial intelligence in improving exercise therapy outcomes for people with type 2 diabetes, type 1 diabetes, and obesity. The source positions digital therapeutics as a bridge between clinical exercise recommendations and sustainable real-world practice, but does not report original efficacy or effectiveness data.
Journal article. People with type 2 diabetes, type 1 diabetes, and obesity undergoing exercise therapy.
Many people do not achieve recommended physical activity levels, and translation of exercise recommendations into everyday life remains challenging App-based lifestyle interventions can combine physical activity promotion, nutritional support, continuous glucose monitoring and behavioural strategies to improve metabolic outcomes Closed-loop systems integrating glucose data, insulin delivery, physical activity, meals and contextual information may reduce burden of exercise-related glucose management
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Clinicians should consider integrating digital health applications and continuous glucose monitoring into exercise prescriptions for diabetes and obesity management. The framework presented suggests digital solutions may improve adherence and personalise support, though implementation requires attention to data quality, validation, and clinical responsibility.
This is a keynote presentation summarizing the current state and potential of digital solutions in exercise therapy for diabetes and obesity, based on existing evidence and expert consensus rather than reporting original research findings.
Quoted from the source exactly as published.
Clinicians should consider integrating digital health applications and continuous glucose monitoring into exercise prescriptions for diabetes and obesity management. The framework presented suggests digital solutions may improve adherence and personalise support, though implementation requires attention to data quality, validation, and clinical responsibility.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Physical activity and structured exercise are essential components of the management of diabetes and obesity (Moser et al., 2020)(Davies et al., 2022). Nevertheless, many people do not achieve recommended activity levels, and the translation of exercise recommendations into everyday life remains challenging. Digital solutions may help to overcome this gap by providing personalised guidance, continuous feedback and scalable support beyond traditional clinical settings (Moser et al., 2025). This keynote will explore how digital health applications, including Digital Health Applications (DiGAs), can be integrated into exercise therapy for people with type 2 diabetes and obesity (Kannenberg et al., 2026). Evidence from clinical studies will illustrate how app-based lifestyle interventions can combine physical activity promotion, nutritional support, continuous glucose monitoring and behavioural strategies to improve metabolic outcomes and facilitate sustainable lifestyle changes. For people with type 1 diabetes, the presentation will focus on the interaction between exercise, continuous glucose monitoring and automated insulin delivery (Zivkovic et al., 2026). Current developments towards fully closed-loop systems will be discussed, with particular attention to how glucose data, insulin delivery, physical activity, meals and contextual information can be merged into integrated decision-support systems (Bünzel et al., 2026). Such approaches may reduce the burden of exercise-related glucose management and enable safer and more personalised participation in physical activity. Finally, the emerging role of artificial intelligence and large language models in diabetes, obesity and exercise research will be addressed. Potential applications include the analysis of complex multimodal datasets, personalised exercise recommendations, clinical decision support and the development of new research approaches. Alongside these opportunities, important limitations relating to data quality, validation, transparency and clinical responsibility will be considered. Overall, digital therapeutics, connected diabetes technologies and artificial intelligence have the potential to transform exercise therapy from a largely standardised intervention into a more personalised, adaptive and continuously supported component of diabetes and obesity care. Their successful implementation may help translate scientific evidence into accessible, safe and sustainable real-world physical activity support by bridging the gap between clinical recommendations and daily life, while enabling more individualised guidance, continuous feedback and informed therapeutic decision-making. References Bünzel, K., Sanfilippo, S., Schierbauer, J., Schuster, L., Sourij, H., Tauschmann, M., Leb-Stöger, U., Seemann, A., Tschakert, G., Birnbaumer, P., Kapan, A., & Moser, O. (2026). Long-Term Associations Between Energy Expenditure and CGM-Derived Metrics in Adults With Type 1 Diabetes: A Retrospective Analysis of the Syntactiq Cockpit Database. Diabetes, Obesity & Metabolism. https://doi.org/10.1111/DOM.71003 Davies, M. J., Aroda, V. R., Collins, B. S., Gabbay, R. A., Green, J., Maruthur, N. M., Rosas, S. E., Prato, S. Del, Mathieu, C., Mingrone, G., Rossing, P., Tankova, T., Tsapas, A., & Buse, J. B. (2022). Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care, 45(11), 2753–2786. https://doi.org/10.2337/DCI22-0034 Kannenberg, S., Voggel, J., Thieme, N., Witt, O., Pethahn, K. L., Schütt, M., Sina, C., Freckmann, G., & Schröder, T. (2026). Unlocking Potential: Personalized Lifestyle Therapy for Type 2 Diabetes Through a Predictive Algorithm-Driven Digital Therapeutic. Journal of Diabetes Science and Technology, 20(1), 113–123. https://doi.org/10.1177/19322968241266821 Moser, O., Eckstein, M. L., West, D. J., Goswami, N., Sourij, H., & Hofmann, P. (2020). Type 1 Diabetes and Physical Exercise: Moving (forward) as an Adjuvant Therapy. Current Pharmaceutical Design, 26(9), 946–957. https://doi.org/10.2174/1381612826666200108113002 Moser, O., Zaharieva, D. P., Adolfsson, P., Battelino, T., Bracken, R. M., Buckingham, B. A., Danne, T., Davis, E. A., Dovč, K., Forlenza, G. P., Gillard, P., Hofer, S. E., Hovorka, R., Jacobs, P. G., Mader, J. K., Mathieu, C., Nørgaard, K., Oliver, N. S., O’Neal, D. N., … Riddell, M. C. (2025). The use of automated insulin delivery around physical activity and exercise in type 1 diabetes: a position statement of the European Association for the Study of Diabetes (EASD) and the International Society for Pediatric and Adolescent Diabetes (ISPAD). Diabetologia, 68(2), 255–280. https://doi.org/10.1007/S00125-024-06308-Z Zivkovic, J., Mitter, M., Theodorou, D., Moser, O., & Glatzer, T. (2026). Exercise in type 1 diabetes: real-world data on glucose levels and hypoglycaemia risk from over 420,000 exercise sessions. Diabetologia, 69(6), 1457–1467. https://doi.org/10.1007/S00125-026-06672-Y
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