- Email: [email protected]
Measuring the energy cost of children's play: Steps or counts?
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Thursday 1 November Papers / Journal of Science and Medicine in Sport 15 (2013) S34–S126
and methods remains inconsistent. In this study we investigated the impact of different data reduction criteria on participant inclusion and PA data outcomes. Methods: We used data from the Danish SPACE for physical activity study (n = 1,348,11–13y). Adolescents wore the Actigraph GT3X for seven consecutive days. Accelerometer data were analyzed using a range of values for three key data reduction issues: number of valid days (1, 2, 3, 4, 5, 6 and 7days), daily wear time (6, 8, 9, 10 and 12 h/day) and non-wear time (10, 20, 30, 60 and 90 min of consecutive zeroes). The open source software Propero Actigraph Data Analyzer was used to compare the effects of the selected criteria on participant inclusion and PA outcomes (mean cpm). The following parameters in the data reduction analyses were fixed: 30 sec epoch, 24 h duration, first registration accepted after 4 h, maximum value 20,000cpm, and two activity epochs permitted in blocks of non-wear. Results: Accelerometer data were obtained from a total of 1,296 adolescents. Descriptive analyses showed that increasing minimum daily wear time and number of valid days resulted in a lower percentage of participants included for analysis. On average, 98.3% of participants had at least 1 valid day, 90.6% had 4 days, and 51.3% had 7 days. Lengthening non-wear duration resulted in a higher percentage of participants included. In general we found the most substantial differences in compliance when looking at 10–12 h (daily wear time), and 5–7days (number of valid days). Only 4.2% of participants had 7 valid days of 12 h wear time, whereas 98.8% of participants had at least 1 valid day of 6 h wear time using a 10 min non-wear criterion. The corresponding numbers using a 90 min non-wear criterion were 20.6% and 99.4%. Lengthening the non-wear period decreases PA level (mean cpm) substantially, e.g. average PA was 641 cpm (5 days of 10 h) using the 10 min non-wear criterion compared to 570 cpm using 90 min non-wear. No systematic differences in PA outcomes were found when comparing the range of days and hours. Discussion: We used a systematic approach to illustrate that even small inconsistencies in accelerometer data reduction can have substantial impact on compliance and PA outcomes. Optimal data processing techniques will depend significantly on the research question to be answered. Support: TrygFonden supported the project.
whole-room calorimeter–combined with direct observation as the criterion measures. Methods: 38 children aged 4–6 years (5.3 ± 1.0 years) completed a ∼150 min whole-room calorimeter protocol involving age-appropriate SB, LPA and MVPA (e.g. watching a movie, drawing on a whiteboard, running on the spot, modified basketball shooting). Each child wore an ActiGraph GT3X. Physical activity intensity was classified using the following ActiGraph cut-points: Pate (MVPA: ≥420 counts/15s SB: ≤37 counts/15s); Evenson (MVPA: ≥574 counts/15s SB: ≤25 counts/15s); Van Cauwenberghe (MVPA: ≥585 counts/15s SB: ≤372 counts/15s); Sirard (MVPA: ≥891 counts/15s SB: ≤398 counts/15s); Puyau (MVPA: ≥800 counts/15s SB: < 200 counts/15s); and; Reilly (SB: < 275 counts/15s). Data were reintegrated to 15- or 60-second epochs depending on the specific cut-point. Classification accuracy was evaluated using weighted Kappa statistics, sensitivity, specificity, and area under the receiver operating characteristics curve. Results: Pate (=0.65) and Evenson (=0.68) cut-points exhibited significantly better agreement than Van Cauwenberghe (=0.39), Sirard (=0.35) and Puyau (=0.52) across all intensities. Classification accuracy for MVPA was significantly higher for the Pate cut-point (ROC-AUC = 0.78, 95% CI = 0.77–0.79) compared to the others (ROC-AUC = 0.68–0.76). The accuracy for SB was significantly higher for the Pate (ROC-AUC = 0.89, 95% CI = 0.89–0.90) and Evenson (ROC-AUC = 0.90, 95% CI = 0.89–0.90) cut-points compared to the others (ROC-AUC = 0.69–0.80). Combining ≤25 counts/15s for SB and ≥420 counts/15s for MVPA resulted in significantly higher classification accuracy (ROC-AUC = 0.72, 95% CI = 0.72–0.73) for LPA compared to Pate, Van Cauwenberghe, Sirard and Puyau cut-points (ROC-AUC = 0.55–0.71). Only the Pate and Evenson cutpoints exhibited fair classification accuracy for all intensities. Discussion: On the basis of these findings we recommend that researchers use cut-points of ≤25, > 25 and < 420, and ≥420 counts/15s to classify SB, LPA and MVPA, respectively, in preschool children when using the ActiGraph. Using consistent cut-points across studies will result in better classification of children’s sedentary behaviour and physical activity as well as greater comparability between studies.
http://dx.doi.org/10.1016/j.jsams.2012.11.159
158
157
Measuring the energy cost of children’s play: Steps or counts?
Comparison of ActiGraph cut-points for predicting physical activity intensity in preschool children Janssen 1,∗ ,
Cliff 1 ,
Okely 1 ,
Hinkley 1 ,
X. D. T. T. M. Batterham 1 , U. Ekelund 3 , S. Brage 3
J.
Reilly 2 ,
R.
Jones 1 ,
1
University of Wollongong University of Strathclyde 3 MRC Epidemiology Unit 2
Introduction: Currently several ActiGraph accelerometer cutpoints are available to classify physical activity intensity among preschoolers. This may result in meaningful differences when estimating time spent in different intensities of physical activity. To overcome this, comparative validity studies are needed to simultaneously compare the different cut-points against a criterion measure. This study evaluated the classification accuracy of five light (LPA) and moderate-to-vigorous physical activity (MVPA) and six sedentary behaviour (SB) ActiGraph cut-point definitions in preschool children using energy expenditure–measured by a
http://dx.doi.org/10.1016/j.jsams.2012.11.160
C. Howe ∗ , K. Ticknor, B. Ragan Ohio University Introduction: Pedometers have been used to motivate people, including children, to increase their daily physical activity (PA) levels and thus, to prevent obesity. However, unlike accelerometers, little has been done to validate the use of pedometers for estimating PA energy expenditure (EE) or PA intensity. This study assessed the accuracy of predicting children’s PAEE and PA intensity from pedometer and accelerometer data compared to indirect calorimetry. Methods: Healthy weight (HW; BMI < 85th %ile) and overweight (OW; BMI≥85th %ile) children played a random selection of 10 games for 6 min each. While playing the games, the children wore a lightweight backpack containing a metabolic unit to measure the oxygen consumption (criterion) and an activity monitor on their hip to measure both accelerometer counts/min and pedometer steps/min. Prediction equations were used to estimate EE from counts/min and steps/min. A RMANOVA was used to assess differences in activity monitor data, estimated EE, and measured EE with
Thursday 1 November Papers / Journal of Science and Medicine in Sport 15 (2013) S34–S126
sex and BMI as main effects (p < 0.05). Pearson’s r correlation was used to examine the relationship among activity monitor data and EE. Results: Twenty-four children (mean ± SD: 9.6 ± 1.3 y; 16 boys; 20 HW children) completed a total of 231 games. The mean(±SEE) EE of the games was 4.56 ± 0.1 kcal/min (range = 3.68 ± 0.25–6.74 ± 0.53 kcal/min), with all of the games classified as moderate-to-vigorous intensity PA (range = 4.05 ± 0.30–7.29 ± 0.67 METs). There was no significant difference in measured EE between sex and BMI groups, whereas boys acquired significantly more counts/min (3654 ± 103 vs 3075 ± 170 counts/min) and more steps/min (72.1 ± 2.0 vs 61.3 ± 2.8 steps/min) than girls and HW children acquired significantly more steps/min than OW children (70.5 ± 1.8 vs 58.8 ± 3.9 steps/min). The accelerometer-based (counts/min) prediction equation accurately estimated the EE of the games for all children (p = 0.60) and for each sex and BMI subgroup compared to measured EE. However, the pedometer-based (steps/min) predicted equation consistently overestimated the EE for the games for all groups by 23.0–48.9% (p’s < 0.001–0.0012) compared to measured EE. The relationships between activity monitor data and measured EE were weak for both steps/min and counts/min (R2 = 0.19 and 0.12, respectively). Discussion: According to our findings, using accelerometry data to estimate the energy cost of children’s free-play PA is more accurate than using pedometer data. Due to the nature of children’s free-play PA, pedometry does not capture all the variations in movement patterns to accurately assess the energy cost of the behaviour. http://dx.doi.org/10.1016/j.jsams.2012.11.161
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ranged from 0.46–0.57. Participants under-reported their activity on the single-item measure (-1.59 days) when compared with all objectively measured MVPA, but stronger congruence was observed when compared with MVPA accumulated in bouts of ≥10 minutes (0.38 days). Overall agreement between the single-item and accelerometry in classifying participants as sufficiently/insufficiently active was 58% (k = 0.23, 95% CI = 0.05–0.41) when including all MVPA and 76% (k = 0.39, 95% CI = 0.14–0.64) when including activity undertaken in bouts of ≥10 minutes. Discussion: Correlations between the new single-item measure and accelerometry were stronger than previously reported for many other self-report tools. Agreement between the selfreport and objective measure was reasonable, however agreement varied depending on whether all minutes of MVPA or only sustained bouts of ≥10 minutes were included in the analyses. When including activity undertaken in sustained bouts of ≥10 minutes, consistent with the government recommendation, the single-item measure correctly identified over 80% of insufficiently active participants. These results suggest that the single-item measure is a valid screening tool to determine whether respondents are sufficiently active to benefit their health. http://dx.doi.org/10.1016/j.jsams.2012.11.162 160 Agreement between accelerometer-determined sedentary time and self-report measures: Project STAND S. Biddle 1,∗ , C. Edwardson 1,2,3 , M. Davies 2,3 , T. Khunti 2,3 , M. Nimmo 1,3 , E. Wilmot 2,3 , T. Yates 2,3
Gorely 1 , K.
1
159 Can a single question provide an accurate measure of physical activity? K. Milton 1,∗ , S. Clemes 1 , F. Bull 2 1 2
Loughborough University The University of Western Australia
Introduction: In order to collect data from a broad range of settings and across a range of interventions there is a need for short measurement tools which are practical and easy to complete. The single-item measure was developed to capture an assessment of physical activity using one question only. Although this tool has demonstrated strong repeatability and moderately strong validity against other self-report tools, further testing was warranted to determine whether the tool accurately assesses ‘true’ physical activity levels. The aim of the current study was to test the criterion validity of the single-item measure compared with accelerometry. Methods: Participants (n = 66, 65% female, age: 39 ± 11 years) wore an accelerometer (ActiGraph GT3X) over a 7-day period and on day 8 completed the single-item measure. The number of days of ≥30 minutes of accelerometer-determined moderate–vigorous intensity physical activity (MVPA) were calculated using two approaches; firstly by including all minutes of MVPA and secondly by including only MVPA accumulated in bouts of ≥10 minutes (counts/minute ≥1952). Associations between the single-item measure and accelerometer were examined using Spearman correlations and 95% Limits of Agreement. Percent agreement and kappa statistic were used to assess agreement between the tools in classifying participants as sufficiently/insufficiently active. Results: Correlations between the number of days of ≥30 minutes MVPA recorded by the single-item and accelerometer
Loughborough University University of Leicester 3 Leicester-Loughborough Lifestyle NIHR Biomedical Research Unit 2
Introduction: The measurement of sedentary (‘sitting’) time is challenging and current recommendations suggest use of both objective and self-report tools, depending on the research question. The aim of the present study was to determine the degree of agreement for two self-report measures with sedentary time determined by the ActiGraph GT3X + accelerometer in young adults at risk of diabetes. Data are from baseline measures of a RCT–Project STAND. Methods: 193 participants (64% female, age: 32.5 ± 5.6 years, BMI: 34.6 ± 5.0 kg/m2 ) wore an ActiGraph GT3X accelerometer whilst continuing with their normal routine for 10 days. The ActiGraph was positioned on the right hip using an elastic belt. Accelerometer-determined sedentary time was calculated using the cut-point of 100 counts per minute. Accelerometer-determined sedentary times were compared to total daily sitting-related sedentary time measured by the International Physical Activity Questionnaire (IPAQ) and the Domain-Specific Sedentary Behaviour measure of Marshall et al. (MSSE, 2010). For the Marshall scale, an aggregated score was created by summing time reported for sedentary travel, sitting at work, TV viewing, and non-work use of a computer. Accelerometer derived sedentary time was compared to self-report measures using Spearman correlations and Bland-Altman plots to assess mean bias and limits of agreement. Results: There were moderate and significant associations between accelerometer sedentary time and IPAQ (rho = .326, p < .01) and Marshall score (rho = .311, p < .01). Accelerometer sedentary time (M = 615.07 ± 103.94 mins) was overestimated by only 22 mins using the Marshall scale (n = 128; limits of agreement from -518 to 560 mins). Accelerometer determined sedentary
Thursday 1 November Papers / Journal of Science and Medicine in Sport 15 (2013) S34–S126
and methods remains inconsistent. In this study we investigated the impact of different data reduction criteria on participant inclusion and PA data outcomes. Methods: We used data from the Danish SPACE for physical activity study (n = 1,348,11–13y). Adolescents wore the Actigraph GT3X for seven consecutive days. Accelerometer data were analyzed using a range of values for three key data reduction issues: number of valid days (1, 2, 3, 4, 5, 6 and 7days), daily wear time (6, 8, 9, 10 and 12 h/day) and non-wear time (10, 20, 30, 60 and 90 min of consecutive zeroes). The open source software Propero Actigraph Data Analyzer was used to compare the effects of the selected criteria on participant inclusion and PA outcomes (mean cpm). The following parameters in the data reduction analyses were fixed: 30 sec epoch, 24 h duration, first registration accepted after 4 h, maximum value 20,000cpm, and two activity epochs permitted in blocks of non-wear. Results: Accelerometer data were obtained from a total of 1,296 adolescents. Descriptive analyses showed that increasing minimum daily wear time and number of valid days resulted in a lower percentage of participants included for analysis. On average, 98.3% of participants had at least 1 valid day, 90.6% had 4 days, and 51.3% had 7 days. Lengthening non-wear duration resulted in a higher percentage of participants included. In general we found the most substantial differences in compliance when looking at 10–12 h (daily wear time), and 5–7days (number of valid days). Only 4.2% of participants had 7 valid days of 12 h wear time, whereas 98.8% of participants had at least 1 valid day of 6 h wear time using a 10 min non-wear criterion. The corresponding numbers using a 90 min non-wear criterion were 20.6% and 99.4%. Lengthening the non-wear period decreases PA level (mean cpm) substantially, e.g. average PA was 641 cpm (5 days of 10 h) using the 10 min non-wear criterion compared to 570 cpm using 90 min non-wear. No systematic differences in PA outcomes were found when comparing the range of days and hours. Discussion: We used a systematic approach to illustrate that even small inconsistencies in accelerometer data reduction can have substantial impact on compliance and PA outcomes. Optimal data processing techniques will depend significantly on the research question to be answered. Support: TrygFonden supported the project.
whole-room calorimeter–combined with direct observation as the criterion measures. Methods: 38 children aged 4–6 years (5.3 ± 1.0 years) completed a ∼150 min whole-room calorimeter protocol involving age-appropriate SB, LPA and MVPA (e.g. watching a movie, drawing on a whiteboard, running on the spot, modified basketball shooting). Each child wore an ActiGraph GT3X. Physical activity intensity was classified using the following ActiGraph cut-points: Pate (MVPA: ≥420 counts/15s SB: ≤37 counts/15s); Evenson (MVPA: ≥574 counts/15s SB: ≤25 counts/15s); Van Cauwenberghe (MVPA: ≥585 counts/15s SB: ≤372 counts/15s); Sirard (MVPA: ≥891 counts/15s SB: ≤398 counts/15s); Puyau (MVPA: ≥800 counts/15s SB: < 200 counts/15s); and; Reilly (SB: < 275 counts/15s). Data were reintegrated to 15- or 60-second epochs depending on the specific cut-point. Classification accuracy was evaluated using weighted Kappa statistics, sensitivity, specificity, and area under the receiver operating characteristics curve. Results: Pate (=0.65) and Evenson (=0.68) cut-points exhibited significantly better agreement than Van Cauwenberghe (=0.39), Sirard (=0.35) and Puyau (=0.52) across all intensities. Classification accuracy for MVPA was significantly higher for the Pate cut-point (ROC-AUC = 0.78, 95% CI = 0.77–0.79) compared to the others (ROC-AUC = 0.68–0.76). The accuracy for SB was significantly higher for the Pate (ROC-AUC = 0.89, 95% CI = 0.89–0.90) and Evenson (ROC-AUC = 0.90, 95% CI = 0.89–0.90) cut-points compared to the others (ROC-AUC = 0.69–0.80). Combining ≤25 counts/15s for SB and ≥420 counts/15s for MVPA resulted in significantly higher classification accuracy (ROC-AUC = 0.72, 95% CI = 0.72–0.73) for LPA compared to Pate, Van Cauwenberghe, Sirard and Puyau cut-points (ROC-AUC = 0.55–0.71). Only the Pate and Evenson cutpoints exhibited fair classification accuracy for all intensities. Discussion: On the basis of these findings we recommend that researchers use cut-points of ≤25, > 25 and < 420, and ≥420 counts/15s to classify SB, LPA and MVPA, respectively, in preschool children when using the ActiGraph. Using consistent cut-points across studies will result in better classification of children’s sedentary behaviour and physical activity as well as greater comparability between studies.
http://dx.doi.org/10.1016/j.jsams.2012.11.159
158
157
Measuring the energy cost of children’s play: Steps or counts?
Comparison of ActiGraph cut-points for predicting physical activity intensity in preschool children Janssen 1,∗ ,
Cliff 1 ,
Okely 1 ,
Hinkley 1 ,
X. D. T. T. M. Batterham 1 , U. Ekelund 3 , S. Brage 3
J.
Reilly 2 ,
R.
Jones 1 ,
1
University of Wollongong University of Strathclyde 3 MRC Epidemiology Unit 2
Introduction: Currently several ActiGraph accelerometer cutpoints are available to classify physical activity intensity among preschoolers. This may result in meaningful differences when estimating time spent in different intensities of physical activity. To overcome this, comparative validity studies are needed to simultaneously compare the different cut-points against a criterion measure. This study evaluated the classification accuracy of five light (LPA) and moderate-to-vigorous physical activity (MVPA) and six sedentary behaviour (SB) ActiGraph cut-point definitions in preschool children using energy expenditure–measured by a
http://dx.doi.org/10.1016/j.jsams.2012.11.160
C. Howe ∗ , K. Ticknor, B. Ragan Ohio University Introduction: Pedometers have been used to motivate people, including children, to increase their daily physical activity (PA) levels and thus, to prevent obesity. However, unlike accelerometers, little has been done to validate the use of pedometers for estimating PA energy expenditure (EE) or PA intensity. This study assessed the accuracy of predicting children’s PAEE and PA intensity from pedometer and accelerometer data compared to indirect calorimetry. Methods: Healthy weight (HW; BMI < 85th %ile) and overweight (OW; BMI≥85th %ile) children played a random selection of 10 games for 6 min each. While playing the games, the children wore a lightweight backpack containing a metabolic unit to measure the oxygen consumption (criterion) and an activity monitor on their hip to measure both accelerometer counts/min and pedometer steps/min. Prediction equations were used to estimate EE from counts/min and steps/min. A RMANOVA was used to assess differences in activity monitor data, estimated EE, and measured EE with
Thursday 1 November Papers / Journal of Science and Medicine in Sport 15 (2013) S34–S126
sex and BMI as main effects (p < 0.05). Pearson’s r correlation was used to examine the relationship among activity monitor data and EE. Results: Twenty-four children (mean ± SD: 9.6 ± 1.3 y; 16 boys; 20 HW children) completed a total of 231 games. The mean(±SEE) EE of the games was 4.56 ± 0.1 kcal/min (range = 3.68 ± 0.25–6.74 ± 0.53 kcal/min), with all of the games classified as moderate-to-vigorous intensity PA (range = 4.05 ± 0.30–7.29 ± 0.67 METs). There was no significant difference in measured EE between sex and BMI groups, whereas boys acquired significantly more counts/min (3654 ± 103 vs 3075 ± 170 counts/min) and more steps/min (72.1 ± 2.0 vs 61.3 ± 2.8 steps/min) than girls and HW children acquired significantly more steps/min than OW children (70.5 ± 1.8 vs 58.8 ± 3.9 steps/min). The accelerometer-based (counts/min) prediction equation accurately estimated the EE of the games for all children (p = 0.60) and for each sex and BMI subgroup compared to measured EE. However, the pedometer-based (steps/min) predicted equation consistently overestimated the EE for the games for all groups by 23.0–48.9% (p’s < 0.001–0.0012) compared to measured EE. The relationships between activity monitor data and measured EE were weak for both steps/min and counts/min (R2 = 0.19 and 0.12, respectively). Discussion: According to our findings, using accelerometry data to estimate the energy cost of children’s free-play PA is more accurate than using pedometer data. Due to the nature of children’s free-play PA, pedometry does not capture all the variations in movement patterns to accurately assess the energy cost of the behaviour. http://dx.doi.org/10.1016/j.jsams.2012.11.161
S67
ranged from 0.46–0.57. Participants under-reported their activity on the single-item measure (-1.59 days) when compared with all objectively measured MVPA, but stronger congruence was observed when compared with MVPA accumulated in bouts of ≥10 minutes (0.38 days). Overall agreement between the single-item and accelerometry in classifying participants as sufficiently/insufficiently active was 58% (k = 0.23, 95% CI = 0.05–0.41) when including all MVPA and 76% (k = 0.39, 95% CI = 0.14–0.64) when including activity undertaken in bouts of ≥10 minutes. Discussion: Correlations between the new single-item measure and accelerometry were stronger than previously reported for many other self-report tools. Agreement between the selfreport and objective measure was reasonable, however agreement varied depending on whether all minutes of MVPA or only sustained bouts of ≥10 minutes were included in the analyses. When including activity undertaken in sustained bouts of ≥10 minutes, consistent with the government recommendation, the single-item measure correctly identified over 80% of insufficiently active participants. These results suggest that the single-item measure is a valid screening tool to determine whether respondents are sufficiently active to benefit their health. http://dx.doi.org/10.1016/j.jsams.2012.11.162 160 Agreement between accelerometer-determined sedentary time and self-report measures: Project STAND S. Biddle 1,∗ , C. Edwardson 1,2,3 , M. Davies 2,3 , T. Khunti 2,3 , M. Nimmo 1,3 , E. Wilmot 2,3 , T. Yates 2,3
Gorely 1 , K.
1
159 Can a single question provide an accurate measure of physical activity? K. Milton 1,∗ , S. Clemes 1 , F. Bull 2 1 2
Loughborough University The University of Western Australia
Introduction: In order to collect data from a broad range of settings and across a range of interventions there is a need for short measurement tools which are practical and easy to complete. The single-item measure was developed to capture an assessment of physical activity using one question only. Although this tool has demonstrated strong repeatability and moderately strong validity against other self-report tools, further testing was warranted to determine whether the tool accurately assesses ‘true’ physical activity levels. The aim of the current study was to test the criterion validity of the single-item measure compared with accelerometry. Methods: Participants (n = 66, 65% female, age: 39 ± 11 years) wore an accelerometer (ActiGraph GT3X) over a 7-day period and on day 8 completed the single-item measure. The number of days of ≥30 minutes of accelerometer-determined moderate–vigorous intensity physical activity (MVPA) were calculated using two approaches; firstly by including all minutes of MVPA and secondly by including only MVPA accumulated in bouts of ≥10 minutes (counts/minute ≥1952). Associations between the single-item measure and accelerometer were examined using Spearman correlations and 95% Limits of Agreement. Percent agreement and kappa statistic were used to assess agreement between the tools in classifying participants as sufficiently/insufficiently active. Results: Correlations between the number of days of ≥30 minutes MVPA recorded by the single-item and accelerometer
Loughborough University University of Leicester 3 Leicester-Loughborough Lifestyle NIHR Biomedical Research Unit 2
Introduction: The measurement of sedentary (‘sitting’) time is challenging and current recommendations suggest use of both objective and self-report tools, depending on the research question. The aim of the present study was to determine the degree of agreement for two self-report measures with sedentary time determined by the ActiGraph GT3X + accelerometer in young adults at risk of diabetes. Data are from baseline measures of a RCT–Project STAND. Methods: 193 participants (64% female, age: 32.5 ± 5.6 years, BMI: 34.6 ± 5.0 kg/m2 ) wore an ActiGraph GT3X accelerometer whilst continuing with their normal routine for 10 days. The ActiGraph was positioned on the right hip using an elastic belt. Accelerometer-determined sedentary time was calculated using the cut-point of 100 counts per minute. Accelerometer-determined sedentary times were compared to total daily sitting-related sedentary time measured by the International Physical Activity Questionnaire (IPAQ) and the Domain-Specific Sedentary Behaviour measure of Marshall et al. (MSSE, 2010). For the Marshall scale, an aggregated score was created by summing time reported for sedentary travel, sitting at work, TV viewing, and non-work use of a computer. Accelerometer derived sedentary time was compared to self-report measures using Spearman correlations and Bland-Altman plots to assess mean bias and limits of agreement. Results: There were moderate and significant associations between accelerometer sedentary time and IPAQ (rho = .326, p < .01) and Marshall score (rho = .311, p < .01). Accelerometer sedentary time (M = 615.07 ± 103.94 mins) was overestimated by only 22 mins using the Marshall scale (n = 128; limits of agreement from -518 to 560 mins). Accelerometer determined sedentary