Advances in physical activity monitoring and lifestyle interventions in obesity: A review

Department of Human Biology, Maastricht University, Maastricht, The Netherlands.
International journal of obesity (2005) (Impact Factor: 5). 05/2011; 36(2):167-77. DOI: 10.1038/ijo.2011.99
Source: PubMed


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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    • "Methods to predict activity-related energy expenditure have advanced from linear regression to innovative algorithms capable of determining physical activity types and the related metabolic costs. These novel techniques can measure the engagement in specific activity types[51]. Integrated into T2DM therapy, the therapy adherence to physical activity lifestyle interventions could be monitored. In T1DM, these new tech‐ niques could help to estimate the possibly required insulin reduction prior to sports using earlier recordings of similar intensive activities. "
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    ABSTRACT: Diabetes mellitus (DM) is a growing global disease which highly affects the individual patient and represents a global health burden with financial impact on national health care systems. Type 1 DM can only be treated with insulin, whereas for patients with type 2 DM a wide range of therapeutic options are available. These options include lifestyle changes such as change of diet and an increase of physical activity, but also administration of oral or injectable antidiabetic drugs. The diabetes therapy, especially with insulin, is complex. Therapy decisions include various medical and life-style related information. Computerized decision support systems (CDSS) aim to improve the treatment process in patient’s self-management but also in institutional care. Therefore, the personalization of the patient’s diabetes treatment is possible at different levels. It can provide medication support and therapy control, which aid to correctly estimate the personal medication requirements and improves the adherence to therapy goals. It also supports long-term disease management, aiming to develop a personalization of care according to the patient’s risk stratification. Personalization of therapy is also facilitated by using new therapy aids like food and activity recognition systems, lifestyle support tools and pattern recognition for insulin therapy optimization. In this work we cover relevant parameters to personalize diabetes therapy, how CDSS can support the therapy process and the role of machine learning in this context. Moreover, we identify open problems and challenges for the personalization of diabetes therapy with focus on decision support systems and machine learning technology.
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    • "Accelerometers can be used to study patterns of activity in time. A new generation of accelerometers will provide information on body posture and activity recognition to allow objective assessment of subjects’ habitual activities, options for a healthy change, and effects of the follow-up of any changes (Bonomi and Westerterp, 2012). Simultaneous measurement of body acceleration and heart rate can give information on physical fitness (Plasqui and Westerterp, 2006). "
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    ABSTRACT: Physical activity is defined as any bodily movement produced by skeletal muscles that results in energy expenditure. The doubly labeled water method for the measurement of total energy expenditure (TEE), in combination with resting energy expenditure, is the reference for physical activity under free-living conditions. To compare the physical activity level (PAL) within and between species, TEE is divided by resting energy expenditure resulting in a figure without dimension. The PAL for sustainable lifestyles ranges between a minimum of 1.1-1.2 and a maximum of 2.0-2.5. The average PAL increases from 1.4 at age 1 year to 1.7-1.8 at reproductive age and declines again to 1.4 at age 90 year. Exercise training increases PAL in young adults when energy balance is maintained by increasing energy intake. Professional endurance athletes can reach PAL values around 4.0. Most of the variation in PAL between subjects can be ascribed to predisposition. A higher weight implicates higher movement costs and less body movement but not necessarily a lower PAL. Changes in physical activity primarily affect body composition and to a lesser extent body weight. Modern man has a similar PAL as a wild mammal of a similar body size.
    Full-text · Article · Apr 2013 · Frontiers in Physiology
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    • "The strength of this study was that free-living physical activity was measured before and after weight loss using an objective and validated method, which allowed both an assessment of the total amount of body movement and a definition of the individuals’ activity behavior. The activity classification system employed to identify activity types has been successfully validated in free-living individuals [33]. However, the lack of a reference technique to determine duration of certain activity types like free-living cycling hampers the quantification of the methodological accuracy of the classification tree. "
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    ABSTRACT: Activity energy expenditure (AEE) is the component of daily energy expenditure that is mainly influenced by the amount of physical activity (PA) and by the weight of the body displaced. This study aimed at analyzing the effect of weight loss on PA and AEE. The body weight and PA of 66 overweight and obese subjects were measured at baseline and after 12 weeks of 67% energy restriction. PA was measured using a tri-axial accelerometer for movement registration (Tracmor) and quantified in activity counts. Tracmor recordings were also processed using a classification algorithm to recognize 6 common activity types engaged in during the day. A doubly-labeled water validated equation based on Tracmor output was used to estimate AEE. After weight loss, body weight decreased by 13±4%, daily activity counts augmented by 9% (95% CI: +2%, +15%), and this increase was weakly associated with the decrease in body weight (R(2) = 7%; P<0.05). After weight loss subjects were significantly (P<0.05) less sedentary (-26 min/d), and increased the time spent walking (+11 min/d) and bicycling (+4 min/d). However, AEE decreased by 0.6±0.4 MJ/d after weight loss. On average, a 2-hour/day reduction of sedentary time by increasing ambulatory and generic activities was required to restore baseline levels of AEE. In conclusion, after weight loss PA increased but the related metabolic demand did not offset the reduction in AEE due to the lower body weight. Promoting physical activity according to the extent of weight loss might increase successfulness of weight maintenance.
    Full-text · Article · Mar 2013 · PLoS ONE
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