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Assessment of the role of active commuting in meeting physical activity guidelines
Thursday 1 November Posters / Journal of Science and Medicine in Sport 15 (2012) S127–S187
evenings, and community partnerships with local sporting organisations. The following outcomes were assessed at baseline and will be repeated at 6- (mid-intervention) and 12-months (postintervention): physical activity (accelerometers), FMS proficiency (TGMD II) and cardio-respiratory fitness (20 m shuttle test) are the primary outcomes, and secondary outcomes include body mass index (BMI), self-concept (global self-worth and athletic competence) (questionnaire), resilience (questionnaire), enjoyment of PA (questionnaire), social support for PA (questionnaire), and screen time (questionnaire). Activity and instruction time in physical education will be assessed using the System for Observing Fitness Instruction Time. Results/discussion: SCORES is an innovative school-based physical activity and FMS intervention designed to support students attending schools in low-income communities to be more skilled and active. The findings from the study may be used to guide teacher pre-service education, professional development and school policy in primary schools. http://dx.doi.org/10.1016/j.jsams.2012.11.453 451 Assessment of the role of active commuting in meeting physical activity guidelines M. Granat ∗ , G. MacLellan Glasgow Caledonian University Introduction: Walking for transport to work, both for complete journeys and for travel to and from public transport stops, has been shown to contribute to increased physical activity [PA] in terms of number of steps and time spent in moderate to vigorous PA [MVPA]. Much of this evidence has been based on self-report data or obtained from travel survey information. The aim of this study was to explore, using objective data, the contribution that active commuting makes to total accumulated steps and to total MVPA by combining data from an activity monitor with precise location information from a GPS system. Methods: 26 office workers [17F; mean age 38 (range 23–65)], in the greater Glasgow area, wore an activPALTM activity monitor and a GPS systems continuously for 7 days. Commuting periods were determined from the GPS data. Duration of MVPA was calculated using time spent in stepping at a cadence equal to, or greater than, 109 steps/min. Both MVPA and number of steps were calculated for the whole 7 days and for the commuting times alone. Results: On average 32% [SD 11%] of weekly total steps and 66% [SD 23%] of weekly MVPA were due to active commuting. Walking whilst at work made up a further contribution of 40% to total steps and 22% to MVPA totals. There were no significant relationships between distance people had to commute to work and steps or MVPA. Discussion: Active commuting to work contributes a very high proportion of an individual’s weekly MVPA but, makes a more modest contribution to weekly total steps. Only 33% of MVPA comes from non-commuting time and only 12% from activities not
S187
associated with going to or being at the workplace. This has could have an important message for public health interventions and public health strategies. http://dx.doi.org/10.1016/j.jsams.2012.11.454 452 Event-based analysis of free-living behaviour M. Granat ∗ Glasgow Caledonian University Introduction: The quantification of free-living physical activities (PA) is important in understanding how physical activity and sedentary behaviour impact on health and how interventions might modify free-living behaviour to enhance health. Quantification, and the terminology used, has often been determined by the choice of measurement technique. Many systems use cumulated acceleration over fixed epochs resulting in outcomes of counts which do not have any real physical units and can be difficult to interpret. The aims of this paper are to describe a systematic approach for the analysis of PA. Methods: A terminology and a systematic approach for the analysis of free-living activity information based on event-based activity data using a flexible hierarchical classification of events were developed. Results: An event is consecutive periods of activities and all events have an event label, a start time and a duration. All physical activities are initially classified into upright events and sedentary events and at this level analysis can be undertaken separately on each of these events and on the patterning of these two types of events. At the next level upright events are divided into standing events and stepping events. This data stream can then be analysed as a sequence of sedentary and upright events or separately as a sequence of sedentary, standing and stepping events. In addition to start time and duration the stepping event will have another parameter which is the number of steps contained within that stepping event. Average cadence for the stepping event can then become a derived output associated with this event. In the older adult population we have used this approach to understand differences in sub-populations that illuminate fundamental differences in the way in which both sedentary and upright time are accumulated. Here, although there were volumetric differences in the data, there was also a fundamental difference in the pattern of these events providing evidence of differences in behaviour. Discussion: The quantification of free-living behaviour is the result of the analysis on the patterns of these chosen events. Eventbased analysis provides a flexible yet robust method of addressing the research question(s) and provides a deeper insight into freeliving behaviour. It is proposed that it is through event-based analysis we can more clearly understand how behaviour is related to health and we can produce more relevant outcome measures to understand the effectiveness of interventions. http://dx.doi.org/10.1016/j.jsams.2012.11.893
evenings, and community partnerships with local sporting organisations. The following outcomes were assessed at baseline and will be repeated at 6- (mid-intervention) and 12-months (postintervention): physical activity (accelerometers), FMS proficiency (TGMD II) and cardio-respiratory fitness (20 m shuttle test) are the primary outcomes, and secondary outcomes include body mass index (BMI), self-concept (global self-worth and athletic competence) (questionnaire), resilience (questionnaire), enjoyment of PA (questionnaire), social support for PA (questionnaire), and screen time (questionnaire). Activity and instruction time in physical education will be assessed using the System for Observing Fitness Instruction Time. Results/discussion: SCORES is an innovative school-based physical activity and FMS intervention designed to support students attending schools in low-income communities to be more skilled and active. The findings from the study may be used to guide teacher pre-service education, professional development and school policy in primary schools. http://dx.doi.org/10.1016/j.jsams.2012.11.453 451 Assessment of the role of active commuting in meeting physical activity guidelines M. Granat ∗ , G. MacLellan Glasgow Caledonian University Introduction: Walking for transport to work, both for complete journeys and for travel to and from public transport stops, has been shown to contribute to increased physical activity [PA] in terms of number of steps and time spent in moderate to vigorous PA [MVPA]. Much of this evidence has been based on self-report data or obtained from travel survey information. The aim of this study was to explore, using objective data, the contribution that active commuting makes to total accumulated steps and to total MVPA by combining data from an activity monitor with precise location information from a GPS system. Methods: 26 office workers [17F; mean age 38 (range 23–65)], in the greater Glasgow area, wore an activPALTM activity monitor and a GPS systems continuously for 7 days. Commuting periods were determined from the GPS data. Duration of MVPA was calculated using time spent in stepping at a cadence equal to, or greater than, 109 steps/min. Both MVPA and number of steps were calculated for the whole 7 days and for the commuting times alone. Results: On average 32% [SD 11%] of weekly total steps and 66% [SD 23%] of weekly MVPA were due to active commuting. Walking whilst at work made up a further contribution of 40% to total steps and 22% to MVPA totals. There were no significant relationships between distance people had to commute to work and steps or MVPA. Discussion: Active commuting to work contributes a very high proportion of an individual’s weekly MVPA but, makes a more modest contribution to weekly total steps. Only 33% of MVPA comes from non-commuting time and only 12% from activities not
S187
associated with going to or being at the workplace. This has could have an important message for public health interventions and public health strategies. http://dx.doi.org/10.1016/j.jsams.2012.11.454 452 Event-based analysis of free-living behaviour M. Granat ∗ Glasgow Caledonian University Introduction: The quantification of free-living physical activities (PA) is important in understanding how physical activity and sedentary behaviour impact on health and how interventions might modify free-living behaviour to enhance health. Quantification, and the terminology used, has often been determined by the choice of measurement technique. Many systems use cumulated acceleration over fixed epochs resulting in outcomes of counts which do not have any real physical units and can be difficult to interpret. The aims of this paper are to describe a systematic approach for the analysis of PA. Methods: A terminology and a systematic approach for the analysis of free-living activity information based on event-based activity data using a flexible hierarchical classification of events were developed. Results: An event is consecutive periods of activities and all events have an event label, a start time and a duration. All physical activities are initially classified into upright events and sedentary events and at this level analysis can be undertaken separately on each of these events and on the patterning of these two types of events. At the next level upright events are divided into standing events and stepping events. This data stream can then be analysed as a sequence of sedentary and upright events or separately as a sequence of sedentary, standing and stepping events. In addition to start time and duration the stepping event will have another parameter which is the number of steps contained within that stepping event. Average cadence for the stepping event can then become a derived output associated with this event. In the older adult population we have used this approach to understand differences in sub-populations that illuminate fundamental differences in the way in which both sedentary and upright time are accumulated. Here, although there were volumetric differences in the data, there was also a fundamental difference in the pattern of these events providing evidence of differences in behaviour. Discussion: The quantification of free-living behaviour is the result of the analysis on the patterns of these chosen events. Eventbased analysis provides a flexible yet robust method of addressing the research question(s) and provides a deeper insight into freeliving behaviour. It is proposed that it is through event-based analysis we can more clearly understand how behaviour is related to health and we can produce more relevant outcome measures to understand the effectiveness of interventions. http://dx.doi.org/10.1016/j.jsams.2012.11.893