Type-2 diabetes (T2D) is one of the fastest growing chronic diseases worldwide. The PREVIEW project has been initiated to find the most effective lifestyle (diet and physical activity) for the prevention of T2D, in overweight and obese participants with increased risk for T2D. The study is a three-year multi-centre, 2 × 2 factorial, randomised controlled trial. The impact of a high-protein, low-glycaemic index (GI) vs. moderate protein, moderate-GI diet in combination with moderate or high-intensity physical activity on the incidence of T2D and the related clinical end-points are investigated. The intervention started with a two-month weight reduction using a low-calorie diet, followed by a randomised 34-month weight maintenance phase comprising four treatment arms. Eight intervention centres are participating (Denmark, Finland, United Kingdom, The Netherlands, Spain, Bulgaria, Australia, and New Zealand). Data from blood specimens, urine, faeces, questionnaires, diaries, body composition assessments, and accelerometers are collected at months 0, 2, 6, 12, 18, 24, and 36. In total, 2326 adults were recruited. The mean age was 51.6 (SD 11.6) years, 67% were women. PREVIEW is, to date, the largest multinational trial to address the prevention of T2D in pre-diabetic adults through diet and exercise intervention. Participants will complete the final intervention in March, 2018.
AimsThe PREVIEW lifestyle intervention study (http://clinicaltrials.gov Identifier: NCT01777893) is, to date, the largest, multinational study concerning prevention of type‐2 diabetes. We hypothesized that the initial, fixed low‐energy diet (LED) would induce different metabolic outcomes in men vs women.Materials and methodsAll participants followed a LED (3.4 MJ/810 kcal/daily) for 8 weeks (Cambridge Weight Plan). Participants were recruited from 8 sites in Europe, Australia and New Zealand. Those eligible for inclusion were overweight (BMI ≥ 25 kg/m2) individuals with pre‐diabetes according to ADA‐criteria. Outcomes of interest included changes in insulin resistance, fat mass (FM), fat‐free mass (FFM) and metabolic syndrome Z‐score.ResultsIn total, 2224 individuals (1504 women, 720 men) attended the baseline visit and 2020 (90.8%) completed the follow‐up visit. Following the LED, weight loss was 16% greater in men than in women (11.8% vs 10.3%, respectively) but improvements in insulin resistance were similar. HOMA‐IR decreased by 1.50 ± 0.15 in men and by 1.35 ± 0.15 in women (ns). After adjusting for differences in weight loss, men had larger reductions in metabolic syndrome Z‐score, C‐peptide, FM and heart rate, while women had larger reductions in HDL cholesterol, FFM, hip circumference and pulse pressure. Following the LED, 35% of participants of both genders had reverted to normo‐glycaemia.ConclusionsAn 8‐week LED induced different effects in women than in men. These findings are clinically important and suggest gender‐specific changes after weight loss. It is important to investigate whether the greater decreases in FFM, hip circumference and HDL cholesterol in women after rapid weight loss compromise weight loss maintenance and future cardiovascular health.
The current work describes a methodology to automatically detect the severity of bradykinesia in motor disease patients using wireless, wearable accelerometers. This methodology was tested with cross validation through a sample of 20 Parkinson's disease patients. The assessment of methodology was carried out through some daily living activities which were detected using an activity recognition algorithm. The Unified Parkinson's Disease Rating Scale (UPDRS) severity classification of the algorithm coincides between 70 and 86% from that of a trained neurologist depending on the classifier used. These severities were calculated for 5 second segments of the signal with 50% of overlap. A bradykinesia profiler is also presented in this work. This profiler removes the overlap of the segments and calculates the confidence of the resulting events. It also calculates average severity, duration and symmetry values for those events. The profiler has been tested with a bogus dataset. Future work includes better training for the severity classifier with a larger sample and testing the profiler with real, longterm patient data in a projected pilot phase in three European hospitals.
Parkinson's disease (PD) predominantly alters the motor performance of the affected individuals. In particular, the loss of dopaminergic neurons compromises the speed, the automaticity and fluidity of movements. As the disease evolves, PD patient's motion becomes slower and tremoric and the response to medication fluctuates along the day. In addition, the presence of involuntary movements deteriorates voluntary movement in advanced state of the disease. These changes in the motion can be detected by studying the variation of the signals recorded by accelerometers attached in the limbs and belt of the patients. The analysis of the most significant changes in these signals make possible to build an individualized motor profile of the disease, allowing doctors to personalize the medication intakes and consequently improving the response of the patient to the treatment. Several works have been done in a laboratory and supervised environments providing solid results; this work focused on the design of unsupervised method for the assessment of gait in PD patients. The development of a reliable quantitative tool for long-term monitoring of PD symptoms would allow the accurate detection of the clinical status during the different PD stages and the evaluation of motor complications. Besides, it would be very useful both for routine clinical care as well as for novel therapies testing.
The aim of this paper is to describe and present the results of the automatic detection and assessment of bradykinesia in motor disease patients using wireless, wearable accelerometers. The current work is related to a module of the PERFORM system, a FP7 project from the European Commission, that aims at providing an innovative and reliable tool, able to evaluate, monitor and manage patients suffering from Parkinson's disease. The assessment procedure was carried out through a developed C# library that detects the activities of the patient using an activity recognition algorithm and classifies the data using a Support Vector Machine trained with data coming from previous test phases. The accuracy between the output of the automatic detection and the evaluation of the clinician both expressed with the Unified Parkinson's disease Rating Scale, presents an average value of [68.3 ± 8.9]%. A meta-analysis algorithm is used in order to improve the accuracy to an average value of [74.4 ± 14.9]%. Future work will include a personalized training of the classifiers in order to achieve a higher level of accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.