A randomized controlled trial on the effects of combined aerobic-resistance exercise on muscle strength and fatigue, glycemic control and health-related quality of life of type 2 diabetes patients

Aim: to evaluate the effects of a 12-weeks combined aerobic-resistance exercise therapy on fatigue and isokinetic muscle strength, glycemic control and health-related quality of life (HRQoL) in moderately affected type 2 diabetes (T2DM) patients. Methods: a randomized controlled trial design was employed. Forty-three T2DM patients were assigned to an exercise group (n = 22), performing 3 weekly sessions of 60 minutes of combined aerobic-resistance exercise for 12-weeks; or a no exercise control group (n = 21). Both groups were evaluated at a baseline and after 12-weeks of exercise therapy for: 1) muscle strength and fatigue by isokinetic dynamometry; 2) plasma glycated hemoglobin A1C (HbA1C); and 3) HRQoL utilizing the SF-36 questionnaire. Results: the exercise therapy led to improvements in muscle fatigue in knee extensors (-55%) and increased muscle strength in knee flexors and extensors (+15 to +30%), while HbA1C decreased (-18%). In addition, the exercising patients showed sizeable improvements in HRQoL: physical function (+53%), vitality (+21%) and mental health (+40%). Conclusion: 12-weeks of combined aerobic-resistance exercise was highly effective to improve muscle strength and fatigue, glycemic control and several aspects of HRQoL in T2DM patients. These data encourage the use of aerobic and resistance exercise in the good clinical care of T2DM.

Quality of Judgment Assessment

The intersection of Artificial Intelligence and The Law stands for a multifaceted matter, and its effects set the advances on culture, organization, as well as the social matters, when the emergent information technologies are taken into consideration. From this point of view, the weight of formal and informal Conflict Resolution settings should be highlighted, and the use of defective data, information or knowledge must be emphasized. Indeed, it is hard to do it with traditional problem solving methodologies. Therefore, in this work the focus is on the development of decision support systems, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks. It is intended to evaluate the Quality-of-Judgments and the respective Degree-of-Confidence that one has on such happenings.