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: The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.PLoS ONE 01/2015; 10(3):e0118723. DOI:10.1371/journal.pone.0118723 · 3.53 Impact Factor
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ABSTRACT: “Objective” methods to monitor physical activity and sedentary patterns in free-living conditions are necessary to further our understanding of their impacts on health. In recent years, many software solutions capable of automatically identifying activity types from portable accelerometry data have been developed, with promising results in controlled conditions, but virtually no reports on field tests. An automatic classification algorithm initially developed using laboratory-acquired data (59 subjects engaging in a set of 24 standardized activities) to discriminate between 8 activity classes (lying, slouching, sitting, standing, walking, running, and cycling) was applied to data collected in the field. Twenty volunteers equipped with a hip-worn triaxial accelerometer performed at their own pace an activity set that included, among others, activities such as walking the streets, running, cycling, and taking the bus. Performances of the laboratory-calibrated classification algorithm were compared with those of an alternative version of the same model including field-collected data in the learning set. Despite good results in laboratory conditions, the performances of the laboratory-calibrated algorithm (assessed by confusion matrices) decreased for several activities when applied to free-living data. Recalibrating the algorithm with data closer to real-life conditions and from an independent group of subjects proved useful, especially for the detection of sedentary behaviors while in transports, thereby improving the detection of overall sitting (sensitivity: laboratory model = 24.9%; recalibrated model = 95.7%). Automatic identification methods should be developed using data acquired in free-living conditions rather than data from standardized laboratory activity sets only, and their limits carefully tested before they are used in field studies.Journal of Applied Physiology 03/2015; 118(6):716-722. DOI:10.1152/japplphysiol.01189.2013 · 3.43 Impact Factor
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ABSTRACT: Maternal and infant health is a global healthcare problem affecting developing and developed countries alike. Pregnancy complications increase the risk of maternal and infant death, and are associated with adverse outcomes such as miscarriage, stillbirth, and preterm birth. Lifestyle modifications before and during pregnancy have been shown to reduce the risk of pregnancy complications. This paper evaluates the use of wearable sensors as an enabling technology to motivate healthier lifestyle behaviors during pregnancy. Wearable sensors have been used to track health and lifestyle in general populations. Pregnancy is associated with hormonal, physiological and psychological adaptations that need to be taken into account when quantifying lifestyle, and there is to date no solution available to accurately measure lifestyle behaviors during pregnancy. The main challenges lay in developing algorithms and systems capable of dealing with physiological adaptations, lifestyle adaptations, and enabling longitudinal monitoring throughout pregnancy. Addressing these challenges will open new opportunities towards an integrated and personalized approach to behavior change interventions based on sensor data. Putting such solutions in the hands of consumers may unlock a crowdsourcing approach to quantification of known risk factors, discovery of new markers for pregnancy complications, and to answering some of the fundamental questions that remain around adverse outcomes such as preterm birthProceedings of the IEEE 02/2015; 103(2). DOI:10.1109/JPROC.2014.2387017 · 5.47 Impact Factor