Prediction of Activity Mode with Global Positioning System and Accelerometer Data

Troped, Philip J.; Oliveira, Marcelo Silva de; Matthews, Charles E.; Cromley, Ellen K.; Melly, Steven J.; Craig, Bruce A.

doi:10.1249/mss.0b013e318164c407

Cited by 106 publications

(91 citation statements)

References 8 publications

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“…To our knowledge these approaches are novel in this field, probably reflecting the high participant burden associated with collecting detailed, dayby-day behavioral information. Our methods may have wider applicability in the future, however, as researchers increasingly generate behavioral data indirectly from devices such as Global Positioning System (GPS) receivers (Jones et al, 2009, Troped et al, 2008.…”

Section: Discussionmentioning

confidence: 99%

Activity compensation and activity synergy in British 8–13year olds

Goodman

Mackett

Paskins

2011

Preventive Medicine

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word count: 198Main text count: 2345 3 ABSTRACT Objectives. To examine whether children compensate for physical activity (the 'activitystat' hypothesis); or alternatively become more active at other times (activity synergy). In 2002 British children (8-13 years) completed travel and activity diaries and wore accelerometers. This generated 1077 days of data which we analysed between-children (comparing all days) and within-child (comparing pairs of days from the same child). Methods.Results. On week and weekend days, each extra 1% of time in PE/games, school breaks, school active travel, non-school active travel, structured sports and out-of-home play predicted a 0.21 to 0.60% increase in the proportion of the day in moderate-to-vigorous physical activity (MVPA). None of these behaviors showed evidence of partial compensation at other times (all p>0.15). Moreover, each 1% increase in weekday nonschool active travel predicted 0.38% more time in MVPA during the rest of the day (95%CI 0.18, 0.58). This activity synergy reflected children using active travel for playing and visiting friends.Conclusions. Contrary to the 'activitystat' hypothesis, we found no evidence of activity compensation. This suggests that interventions increasing activity in specific behaviors may increase activity overall. The activity synergy of non-school active travel underlines the need for further research into this neglected behavior.

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Section: Discussionmentioning

confidence: 99%

Activity compensation and activity synergy in British 8–13year olds

Goodman

Mackett

Paskins

2011

Preventive Medicine

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“…На 1 осный акселерометр поступает та же информация, что и на шагомер, но с преимуществом оценки уско рения движения в дополнение к простому подсчету шагов; 2 и 3 осные акселерометры реагируют на движение в более широком диапазоне и, таким обра зом, более чувствительны, чем 1 осные [48]. При мониторинге ФА акселерометры нередко комбини руются с другими физиологическими датчиками, например, для измерения частоты сердечных со кращений или температуры, либо используются в составе систем наблюдения для повышения их точ ности в определении ежедневной ФА и расходов энергии [49][50][51].…”

Section: мониторы активностиunclassified

Evaluation of physical activity in patients with chronic obstructive pulmonary disease: recommendations of European Respiratory Society

Редакционная¹

2015

Pulʹmonologiâ (Mosk.)

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РезюмеВ рекомендациях Европейского респираторного общества (ЕРО) приводятся консенсус экспертов ЕРО и современные представления о физической активности (ФА) больных хронической обструктивной болезнью легких (ХОБЛ). Дано определение ФА; показаны вли яние ее снижения на легочную функцию; необходимость госпитализации и летальность у больных ХОБЛ; распространенность низкой ФА и ее корреляции с физическим состоянием больных; подходы к лечению, направленному на улучшение ФА при ХОБЛ; описаны методы оценки ФА при ХОБЛ. Представлены также направления будущих научных исследований: в первую очередь, способность ФА мо дифицировать течение заболевания; методы улучшения переносимости физических нагрузок и уменьшения одышки на фоне нагрузки. SummaryThis European Respiratory Society (ERS) statement provides a comprehensive overview on physical activity in patients with chronic obstructive pul monary disease (COPD). A multidisciplinary Task Force of experts representing the ERS Scientific Group 01.02 ''Rehabilitation and Chronic Care'' determined the overall scope of this statement through consensus. Focused literature reviews were conducted in key topic areas and the final con tent of this Statement was agreed upon by all members. The current knowledge regarding physical activity in COPD is presented, including the def inition of physical activity, the consequences of physical inactivity on lung function decline and COPD incidence, physical activity assessment, prevalence of physical inactivity in COPD, clinical correlates of physical activity, effects of physical inactivity on hospitalisations and mortality, and treatment strategies to improve physical activity in patients with COPD. This Task Force identified multiple major areas of research that need to be addressed further in the coming years. These include, but are not limited to, the disease modifying potential of increased physical activity, and to further understand how improvements in exercise capacity, dyspnoea and self efficacy following interventions may translate into increased physical activity.

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“…It is also should be emphasized that the latter research used also supplementary accelerometer data for classification. This type of information is being widely used in recent researches; sometimes together with preliminary knowledge about the transportation network exist in the study-area (Troped et al, 2008;Gong et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

Multi-Stage Approach to Travel-Mode Segmentation and Classification of GPS Traces

Zhang

Dalyot

Eggert

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents a multi-stage approach toward the robust classification of travel-modes from GPS traces. Due to the fact that GPS traces are often composed of more than one travel-mode, they are segmented to find sub-traces characterized as an individual travel-mode. This is conducted by finding individual movement segments by identifying stops. In the first stage of classification three main travel-mode classes are identified: pedestrian, bicycle, and motorized vehicles; this is achieved based on the identified segments using speed, acceleration and heading related parameters. Then, segments are linked up to form sub-traces of individual travel-mode. After the first stage is achieved, a breakdown classification of the motorized vehicles class is implemented based on sub-traces of individual travel-mode of cars, buses, trams and trains using Support Vector Machines (SVMs) method. This paper presents a qualitative classification of travel-modes, thus introducing new robust and precise capabilities for the problem at hand.

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Prediction of Activity Mode with Global Positioning System and Accelerometer Data

Cited by 106 publications

References 8 publications

Activity compensation and activity synergy in British 8–13year olds

Activity compensation and activity synergy in British 8–13year olds

Evaluation of physical activity in patients with chronic obstructive pulmonary disease: recommendations of European Respiratory Society

Multi-Stage Approach to Travel-Mode Segmentation and Classification of GPS Traces

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