Advances in physical activity monitoring and lifestyle interventions in obesity: a review
ABSTRACT Obesity represents a strong risk factor for developing chronic diseases. Strategies for disease prevention often promote lifestyle changes encouraging participation in physical activity. However, determining what amount of physical activity is necessary for achieving specific health benefits has been hampered by the lack of accurate instruments for monitoring physical activity and the related physiological outcomes. This review aims at presenting recent advances in activity-monitoring technology and their application to support interventions for health promotion. Activity monitors have evolved from step counters and measuring devices of physical activity duration and intensity to more advanced systems providing quantitative and qualitative information on the individuals' activity behavior. Correspondingly, methods to predict activity-related energy expenditure using bodily acceleration and subjects characteristics have advanced from linear regression to innovative algorithms capable of determining physical activity types and the related metabolic costs. These novel techniques can monitor modes of sedentary behavior as well as the engagement in specific activity types that helps to evaluate the effectiveness of lifestyle interventions. In conclusion, advances in activity monitoring have the potential to support the design of response-dependent physical activity recommendations that are needed to generate effective and personalized lifestyle interventions for health promotion.
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ABSTRACT: Rantalainen, T, Ruotsalainen, I, and Virmavirta, M. Effect of weighted vest suit worn during daily activities on running speed, jumping power, and agility in young men. J Strength Cond Res 26(11): 3030-3035, 2012-Previous weighted vest interventions using exercise in addition to hypergravity have been successful in improving postural balance and power production capacity. The purpose of this study was to investigate if hypergravity alone in daily activities excluding sporting activities is effective in improving neuromuscular performance in young adults. Eight male subjects (age = 32 [SD: 6] years, height = 178  cm, and body mass = 81  kg) wore weighted vests 3 d·wk for 3 weeks during waking hours, excluding sporting activities. Control group comprised 9 male subjects (age = 32  years, height = 179  cm, and body mass = 83  kg). Performance was assessed with countermovement jump (body mass normalized peak power), figure-of-8 running test (running time), and running velocity test at baseline and at the end of the intervention. At baseline, the groups did not differ from each other (multivariate analysis of variance [MANOVA] p = 0.828). A significant group × time interaction (MANOVA F = 5.1, p = 0.015) was observed for performance variables. Analysis of covariance indicated that the intervention improved the figure-of-8 running time (p = 0.016) (-2.2 vs. 0.5%), whereas normalized peak power (0.0 vs. 1.6%) and running velocity (1.3 vs. 0.1%) were unaffected (p ≥ 0.095). Wearing weighted vests was effective in slightly improving agility-related performance in young men. Because the effect was small, applying hypergravity only during exercise probably suffices. It appears that a proper volume and intensity of hypergravity could be in the order of 5-10% body weight vest worn during up to 50% of the training sessions for a period of 3-4 weeks.The Journal of Strength and Conditioning Research 01/2012; 26(11):3030-5. DOI:10.1519/JSC.0b013e318245c4c6 · 1.86 Impact Factor
- Archives of internal medicine 03/2012; 172(5):444-6. DOI:10.1001/archinternmed.2011.1477 · 13.25 Impact Factor
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ABSTRACT: In this paper, we introduce a novel nonparametric classification technique based on the use of the Wasserstein distance. The proposed scheme is applied in a biomedical context for the analysis of recorded accelerometer data: the aim is to retrieve three types of periodic activities (walking, biking, and running) from a time-frequency representation of the data. The main interest of the use of the Wasserstein distance lies in the fact that it is less sensitive to the location of the frequency peaks than to the global structure of the frequency pattern, allowing us to detect activities almost independently of their speed or incline. Our system is tested on a 24-subject corpus: results show that the use of Wasserstein distance combined with some supervised learning techniques allows us to compare with some more complex classification systems.IEEE transactions on bio-medical engineering 03/2012; 59(6):1610-9. DOI:10.1109/TBME.2012.2190930 · 2.23 Impact Factor