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Integrating self-report and device-based measures to measure workplace sitting time and sitting patterns
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Thursday 1 November Papers / Journal of Science and Medicine in Sport 15 (2013) S34–S126
293 Validation of a novel, objective measure of occupational sitting G. Ryde ∗ , N. Gilson, A. Suppini, W. Brown The University of Queensland Introduction: Few devices objectively measure sitting time. The aims of this study were to test the validity of a sitting pad (SP) to measure desk based sitting time and ‘transitions’ to and from sitting, against camera derived direct observation; and to compare the data with those from two commonly used devices with inclinometers, the activPAL3TM (AP) and ActiGraph GT3X + (AG). Methods: Personal office chairs of employees (9 women, 4 men, mean age 30 ± 6.5 years) were fitted with a SP and each participant wore an AG and AP. A camera was positioned to record chair based transitions during a prescribed sitting/standing and a free living protocol. Camera and device data were binary coded and mapped against the camera derived timeline (sitting = 0; standing/moving = 1). Mean sitting time and transitions were calculated for each device and intra-class correlations (ICCs) and mean differences between a) the SP and the camera and b) the AP, AG and camera, were compared. Results: During the prescribed protocol, the smallest mean differences compared with the camera were for the SP (sitting time 0.30 ± 0.21 minutes; transitions -0.46 ± 0.78). For free living, both the SP and AP showed excellent levels of agreement with the camera for sitting time (0.999 and 0.990 respectively), while agreement between the camera and the AG was poor (0.257). For number of transitions, there was excellent agreement between the SP, AP and the camera (0.997 and 0.928 respectively), but the agreement between the AG and the camera was poor (0.033). Discussion: The findings indicated that the SP and AP provided highly accurate measures of desk based sitting time and transitions in both the prescribed and free living protocols. In contrast, the AG inclinometer was unable to accurately measure sitting time or transitions in either protocol. The SP offers novel measurement opportunities to assess links between occupational sitting and health outcomes, and future potential as an intervention tool to reduce and break prolonged sitting at desks. http://dx.doi.org/10.1016/j.jsams.2012.11.296
self-report data and assuming time at the workplace is 0800 or 0900–1700 Monday to Friday). Methods: Using SAS 9.3, we combined the activPalTM events (bouts) files (which contain more information regarding pattern than epoch summaries) with diary-recorded work, sleep, and removal times. Due to imprecision, self-reported events (e.g. starting work) often occur during a monitored activity bout, so we classified bouts that occurred > = 50% during the relevant selfreported period as being at the workplace (/not), sleep (/wake) and removed (/worn). We used Bland-Altman analysis to evaluate whether the derived monitor-corrected start and finish times adequately reflect participants’ self-report times, and whether our process differs from using monitor data alone with assumed work times. Results: In total, the study monitored 165 days of activity at the workplace from 41 full-time office workers from one organisation. Median (min, max) time of first starting, and last finishing work were 8:32 (06:49, 13:59) and 17:00 (12:00, 21:31). Excluding instances when the monitor was not worn, the monitor-corrected start and finish times agreed with self-report, with mean differences being small (0.0 and 1.3 minutes, respectively) and 95% Limits of Agreement (LoA) excluding meaningful differences (-4.1 to 4.2 and -5.1 to 7.8 minutes, respectively). Monitor-corrected start and finish times showed limited agreement with assumed times (mean difference [95% LoA]: 3.4 (-110.7, 117.5) for 08:30, -26.6 [-140.7, 87.5] for 09:00 and 2.0 [-139.6, 143.6] for 17:00). Conclusions: Self-reported information can be easily integrated with device-based measures to assess workplace sitting and sitting patterns, with only negligible corrections required to match activPaLTM events files with self-report. Findings also suggested that even in an office-based setting, assumed work periods may inadequately separate workplace from non-workplace activity, and unintentionally attribute non-workplace behaviours, such as active transport, to the workplace. http://dx.doi.org/10.1016/j.jsams.2012.11.297 295 Adult perception of sedentary behavior self-report assessment: Cognitive interviewing the SIT-Q B. Lynch 1,∗ , C. Friedenreich 2 , I. Csizmadi 2
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Integrating self-report and device-based measures to measure workplace sitting time and sitting patterns E. Winkler 1,∗ , E. Eakin 1 , G. Wiesner 2 , D. Dunstan 2 , N. Owen 1,2 , G. Healy 1,2 1
The University of Queensland, Cancer Prevention Research Centre, School of Population Health 2 Baker IDI Heart and Diabetes Institute Background: Recent reviews have noted the importance of collecting sitting time data from both device-based measures (to assess total sitting time and patterns of accumulation), and self-report measures (to capture important domain and behaviourspecific information). However, the practicalities of achieving this are unclear. We describe a method that we developed to combine self-report (diary) information with device-based (activPAL3TM ) data to assess bouts of sitting that occur at the workplace (a key setting for prolonged sitting) and outside the workplace, as part of a pilot study targeting workplace sitting reductions. We evaluate this method and compare it to the usual approach (i.e. without
Baker IDI Heart and Diabetes Institute Department of Population Health Research, Alberta Health Services
Introduction: Currently, there are no rigorously tested, comprehensive questionnaires that assess adult sedentary behaviour across multiple domains. The SIT-Q was developed to address this need: it assesses sedentary behaviour across occupation, transportation, household and leisure-time domains. Methods: Cognitive interviewing is an important, but often overlooked, step in questionnaire development. Qualitative attributes can substantially affect questionnaire validity and reliability. Cognitive interviewing can identify misunderstandings of text that could lead to response error and can provide insight into ways in which respondents comprehend, retrieve and formulate their answer.Three rounds of cognitive interviews were conducted with a convenience sample of 11 Canadian adults. Results: Key themes identified by the cognitive interviewing process included: i) confusion about the use of the word “activity” to describe time spent sitting or lying down; ii) uncertainty about the concept of “double counting” time in different, but simultaneous activities; iii) difficulty separating out different workplace sitting tasks, as multitasking is common; iv) difficulty comprehending
Thursday 1 November Papers / Journal of Science and Medicine in Sport 15 (2013) S34–S126
293 Validation of a novel, objective measure of occupational sitting G. Ryde ∗ , N. Gilson, A. Suppini, W. Brown The University of Queensland Introduction: Few devices objectively measure sitting time. The aims of this study were to test the validity of a sitting pad (SP) to measure desk based sitting time and ‘transitions’ to and from sitting, against camera derived direct observation; and to compare the data with those from two commonly used devices with inclinometers, the activPAL3TM (AP) and ActiGraph GT3X + (AG). Methods: Personal office chairs of employees (9 women, 4 men, mean age 30 ± 6.5 years) were fitted with a SP and each participant wore an AG and AP. A camera was positioned to record chair based transitions during a prescribed sitting/standing and a free living protocol. Camera and device data were binary coded and mapped against the camera derived timeline (sitting = 0; standing/moving = 1). Mean sitting time and transitions were calculated for each device and intra-class correlations (ICCs) and mean differences between a) the SP and the camera and b) the AP, AG and camera, were compared. Results: During the prescribed protocol, the smallest mean differences compared with the camera were for the SP (sitting time 0.30 ± 0.21 minutes; transitions -0.46 ± 0.78). For free living, both the SP and AP showed excellent levels of agreement with the camera for sitting time (0.999 and 0.990 respectively), while agreement between the camera and the AG was poor (0.257). For number of transitions, there was excellent agreement between the SP, AP and the camera (0.997 and 0.928 respectively), but the agreement between the AG and the camera was poor (0.033). Discussion: The findings indicated that the SP and AP provided highly accurate measures of desk based sitting time and transitions in both the prescribed and free living protocols. In contrast, the AG inclinometer was unable to accurately measure sitting time or transitions in either protocol. The SP offers novel measurement opportunities to assess links between occupational sitting and health outcomes, and future potential as an intervention tool to reduce and break prolonged sitting at desks. http://dx.doi.org/10.1016/j.jsams.2012.11.296
self-report data and assuming time at the workplace is 0800 or 0900–1700 Monday to Friday). Methods: Using SAS 9.3, we combined the activPalTM events (bouts) files (which contain more information regarding pattern than epoch summaries) with diary-recorded work, sleep, and removal times. Due to imprecision, self-reported events (e.g. starting work) often occur during a monitored activity bout, so we classified bouts that occurred > = 50% during the relevant selfreported period as being at the workplace (/not), sleep (/wake) and removed (/worn). We used Bland-Altman analysis to evaluate whether the derived monitor-corrected start and finish times adequately reflect participants’ self-report times, and whether our process differs from using monitor data alone with assumed work times. Results: In total, the study monitored 165 days of activity at the workplace from 41 full-time office workers from one organisation. Median (min, max) time of first starting, and last finishing work were 8:32 (06:49, 13:59) and 17:00 (12:00, 21:31). Excluding instances when the monitor was not worn, the monitor-corrected start and finish times agreed with self-report, with mean differences being small (0.0 and 1.3 minutes, respectively) and 95% Limits of Agreement (LoA) excluding meaningful differences (-4.1 to 4.2 and -5.1 to 7.8 minutes, respectively). Monitor-corrected start and finish times showed limited agreement with assumed times (mean difference [95% LoA]: 3.4 (-110.7, 117.5) for 08:30, -26.6 [-140.7, 87.5] for 09:00 and 2.0 [-139.6, 143.6] for 17:00). Conclusions: Self-reported information can be easily integrated with device-based measures to assess workplace sitting and sitting patterns, with only negligible corrections required to match activPaLTM events files with self-report. Findings also suggested that even in an office-based setting, assumed work periods may inadequately separate workplace from non-workplace activity, and unintentionally attribute non-workplace behaviours, such as active transport, to the workplace. http://dx.doi.org/10.1016/j.jsams.2012.11.297 295 Adult perception of sedentary behavior self-report assessment: Cognitive interviewing the SIT-Q B. Lynch 1,∗ , C. Friedenreich 2 , I. Csizmadi 2
294
1 2
Integrating self-report and device-based measures to measure workplace sitting time and sitting patterns E. Winkler 1,∗ , E. Eakin 1 , G. Wiesner 2 , D. Dunstan 2 , N. Owen 1,2 , G. Healy 1,2 1
The University of Queensland, Cancer Prevention Research Centre, School of Population Health 2 Baker IDI Heart and Diabetes Institute Background: Recent reviews have noted the importance of collecting sitting time data from both device-based measures (to assess total sitting time and patterns of accumulation), and self-report measures (to capture important domain and behaviourspecific information). However, the practicalities of achieving this are unclear. We describe a method that we developed to combine self-report (diary) information with device-based (activPAL3TM ) data to assess bouts of sitting that occur at the workplace (a key setting for prolonged sitting) and outside the workplace, as part of a pilot study targeting workplace sitting reductions. We evaluate this method and compare it to the usual approach (i.e. without
Baker IDI Heart and Diabetes Institute Department of Population Health Research, Alberta Health Services
Introduction: Currently, there are no rigorously tested, comprehensive questionnaires that assess adult sedentary behaviour across multiple domains. The SIT-Q was developed to address this need: it assesses sedentary behaviour across occupation, transportation, household and leisure-time domains. Methods: Cognitive interviewing is an important, but often overlooked, step in questionnaire development. Qualitative attributes can substantially affect questionnaire validity and reliability. Cognitive interviewing can identify misunderstandings of text that could lead to response error and can provide insight into ways in which respondents comprehend, retrieve and formulate their answer.Three rounds of cognitive interviews were conducted with a convenience sample of 11 Canadian adults. Results: Key themes identified by the cognitive interviewing process included: i) confusion about the use of the word “activity” to describe time spent sitting or lying down; ii) uncertainty about the concept of “double counting” time in different, but simultaneous activities; iii) difficulty separating out different workplace sitting tasks, as multitasking is common; iv) difficulty comprehending