Using Activity Monitors to Measure Sit-to-Stand Transitions in Overweight/Obese Youth : Medicine & Science in Sports & Exercise

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Using Activity Monitors to Measure Sit-to-Stand Transitions in Overweight/Obese Youth


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Medicine & Science in Sports & Exercise 49(8):p 1592-1598, August 2017. | DOI: 10.1249/MSS.0000000000001266
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Sedentary behavior, which is defined as any waking activity with a sitting or reclining posture that results in energy expenditure less than 1.5 METs (25,32), has recently emerged as a public health priority because of the myriad of physical and mental health outcomes associated with too much sitting, even after adjusting for the amount of physical activity (e.g., 10,24,30,34). Measures of sedentary behavior that are reliable, valid, and feasible for use in large studies are essential for drawing accurate conclusions about the prevalence, influences, and health outcomes of sedentary behavior, in addition to evaluating interventions.

Much of the research on the associations among sedentary behavior and health outcomes in youth has been conducted using indirect (parent, teacher, or self-report) measures of time spent watching TV (34). These studies have shown that watching more than 2 h of TV per day is associated with lower fitness, self-esteem, academic achievement, prosocial behavior, and higher body mass index (34). Indirect measures are feasible for use in large-scale studies; however, they are prone to biased estimates because individuals can over- or underreport sedentary behavior. Further, indirect measures provide information on total sedentary time, but few provide information about the pattern of accumulation of sedentary behavior.

Alternatively, direct measures, such as accelerometers, are often used to measure sedentary behavior in youth. Although findings have been somewhat inconsistent in youth, some evidence suggests that accelerometer-measured sedentary time is important to youth's health, particularly body composition (34). An advantage of using accelerometers to measure sedentary behavior is that they provide information on the way in which youth accumulate sedentary time (e.g., 15,31), which research has shown to also be associated with health outcomes. For example, recent research has shown that more breaks in sitting time, also called sit-to-stand transitions, are associated with lower adiposity, fasting glucose levels, and cardiometabolic risk in youth (6,31). However, these and other studies have used accelerometer cut points to measure sit-to-stand transitions and sedentary bout patterns with little evidence of the validity of these methods for measuring sedentary metrics other than total sedentary time.

Only four studies were identified that investigated the validity of hip-worn accelerometers for measuring sit-to-stand transitions, and all were in adults. Each of these studies found that hip-worn accelerometers substantially overestimated the number of sit-to-stand transitions as compared with activPAL (4,16) or direct observation (20,23). Few studies were identified that investigated the validity of the thigh-worn activPAL for measuring sit-to-stand transitions. These studies found that the activPAL provided valid estimates of transitions but were limited to adults (12,20) or prescribed activities (non–free-living; 2,12). To date, no identified studies have examined the validity of hip- or wrist-worn accelerometers or thigh-worn activPAL for measuring sit-to-stand transitions in free-living youth or in overweight/obese samples. Therefore, the current study advances the literature by examining the validity of accelerometers and activity monitors for measuring sit-to-stand transitions in a youth sample with overweight/obesity. Assessing validity in youth with overweight/obesity is particularly important because they are common targets of sedentary interventions (3), and sedentary interventions may be a more acceptable starting point in this population and a catalyst for physical activity.

A primary aim of the current study was to examine criterion validity of commonly used cut points applied to hip- and wrist-worn accelerometer data for measuring sit-to-stand transitions in youth with overweight or obesity. The normal- and low-frequency (LF) filters of the hip-worn ActiGraph were investigated. In addition, because of the evolving methodology for accelerometer data collection and processing (18,22,36), the current study also aimed to fill a gap in the literature by examining the validity of the inclinometer function of the hip-worn accelerometer for measuring sit-to-stand transitions. Criterion validity of the thigh-worn activPAL for measuring sit-to-stand transitions was also investigated, given that previous studies were limited to adults. Direct observation, the gold standard for assessing changes in posture (4,17), was used as the criterion measure.


Participants and Procedures

Participants included nine youth with obesity (body mass index ≥ 95th percentile; ages 10–17 yr) who were engaged in a behavioral weight management program. Parents provided written informed consent, and children provided written assent. Children were enrolled in the study for the duration of an evening weight management group session (approximately 2 h). The first hour of the weight management session included group exercise activities and active games, and the second hour was spent in a classroom. The group leader was asked to incorporate sit-to-stand transitions during both hours of the session to prevent the youth from sitting or standing for the entire data collection period.

All participants wore three noninvasive physical activity measurement devices (i.e., one hip-worn ActiGraph, one wrist-worn ActiGraph, and one thigh-worn activPAL), and trained research staff coded direct observations of their postures. The same computer clock was used to initialize activity devices within 1-h before data collection and to time synchronize the direct observations. The procedures were approved by the local institutional review board.



Participants wore two GT3X model ActiGraph accelerometers (ActiGraph, Pensacola, FL) for the duration of data collection. One ActiGraph was worn on the right hip, affixed with a belt, and the other ActiGraph was worn on the nondominate wrist, affixed with a watchlike strap. Raw acceleration in G force was recorded at 30 Hz for each of the three axes (vertical, mediolateral, and anteroposterior). Using the ActiLife software, raw accelerometer files were converted into 1-s files denoting the counts recorded per every 1 s for the vertical axis. When downloading the hip-worn ActiGraph, the inclinometer feature was selected, which uses an algorithm from the manufacturer to infer the posture of the participant (i.e., standing, lying, sitting, or not being worn). In addition, the hip-worn ActiGraph was downloaded in regular frequency and LF. The regular frequency algorithm raises the lower end of the frequency bandwidth to exclude “acceleration noise,” whereas the LF option is used to increase sensitivity in populations with slow or light movement by extending the bandwidth (1).


Participants wore one activPAL Micro accelerometer (PAL Technologies, Ltd., Glasgow, UK) for the duration of data collection. The activPAL has good criterion validity for assessing posture (i.e., sitting, standing, and lying) as compared with direct observation (2,9,20) in adult and non–free-living samples. All participants wore the activPAL on the right thigh, affixed with adhesive tape (Tagaderm [3M, Maplewood, MI], similar to a bandage). The event files produced from the activPAL software were used to create second-level files denoting, for each second, whether the participant was sitting, standing, standing, and walking; in a sit-to-stand transition; or in a stand-to-sit transition. The minimum sitting/upright time to define a new posture was 1 s.

Direct observations (criterion)

Research staff coded youth participant postures using in-person direct observations facilitated by a multifunctional digital stopwatch and a synced excel tracking form (XNOTE Stopwatch; available at Activity codes were continuously assigned to youth participants while they were wearing the devices and in view of the coders. Activity was coded according to the following definitions: standing (upright position in which a majority of one's weight is supported by one or both feet), sitting (a position in which one's weight is supported by one's buttocks rather than one's feet and in which one's back is upright), lying (in a horizontal position on a supporting surface, includes push-up position), and out of scene. Sit-to-stand transitions were inferred when the participant went from sitting to standing. Similar to activPAL, the minimum sitting/upright time to define a new posture was 1 s.


Participant sex and age were gathered by self-report during study consenting procedures.

Data Processing and Analyses

Second-level data from each device and direct observation were merged using the synchronized time stamps. A sum aggregation was used to convert the vertical axis counts from the ActiGraphs to 15-s intervals (i.e., epochs) in the Statistical Package for the Social Sciences (version 23; SPSS Inc., Chicago, IL). A 15-s epoch was chosen because youth studies commonly use epochs of 5–30 s, as opposed to adult studies which often use 60-s epochs (5,36). A cut point of ≤25 counts per 15-s epoch was applied to the hip-worn accelerometer data to represent the commonly used 100 counts per minute cut point (e.g., 4,20,27,28,35). For the wrist-worn ActiGraph, a cut point of ≤105 counts per 15-s epoch was derived from a previous study that showed good validity of a similar cut point applied to 5-s epochs for assessing total sedentary time (8). For the hip- and wrist-worn accelerometers, a sit-to-stand transition was defined as a nonsedentary epoch preceded by a sedentary epoch, based on the aforementioned cut points. For the direct observation and activPAL data, each 15-s epoch was considered to be a sit-to-stand transition epoch if at least one sit-to-stand transition occurred during the 15 s. For the epoch-level analyses, a ±1-s epoch window was used such that devices would be considered in agreement with direct observation if off by no more than ±1-s epoch. Next, 2 × 2 confusion matrices were used to visually display false-positives, false-negatives, true-positives, and true-negatives with regard to sit-to-stand transitions. These values were then used to calculate accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for the test devices compared with the direct observation criterion measure.

Because aggregated metrics are commonly used in public health studies (13,14,30,31), the data were also aggregated to the hour level to derive the number of sit-to-stand transitions during the hour. This aggregation step was conducted using the 1-s files for the direct observation and activPAL data and the 15-s epoch-level file for the ActiGraph data. Hours with <15 min of data were excluded from hour-level analyses. Mixed-effects linear regression, adjusted for nesting of hours within participants, was used to compare the mean differences in the number of sit-to-stand transitions between the test measures and the direct observations, as were root-mean-square errors. Further, intraclass correlation coefficients (ICC) were calculated from the covariance estimates of these same models to examine correlations between measures. Finally, Bland–Altman plots were used to graphically examine the hour-level agreement between the test methods and the direct observation, and 95% confidence intervals were calculated, as described by Carstensen et al. (7), to account for the nested data structure.


Epoch-level analyses

The accuracy, the specificity, and the negative predictive value of the ActiGraph Hip, ActiGraph Hip-LF, and ActiGraph Wrist were more than 85% compared with direct observation (see Table 1). Given that a large majority of epochs were not sit-to-stand transitions, these test measures had high true-negative rates (i.e., correctly indicating that the participants not engaged in a sit-to-stand transition). However, sensitivity and positive predictive values were low (<48% and <11%, respectively), meaning that the ActiGraph Hip, ActiGraph Hip-LF, and ActiGraph Wrist were prone to false-positives (i.e., indicating that a sit-to-stand transition occurred when it did not) and to a lesser extent false-negatives (i.e., missing a sit-to-stand transition that occurred). A similar pattern was observed with the ActiGraph Inclinometer, although worse agreement was observed for this test device. The ActiGraph Inclinometer had a very high false-positive rate.

Epoch-level agreement for measuring sit-to-stand transitions in youth with overweight and obesity.

Compared with direct observation, the activPAL had accuracy, specificity, and negative predictive values over 96%; the sensitivity and positive predictive values were lower (52.94% and 44.12%, respectively), indicating that the activPAL was somewhat prone to false-positives and to a lesser extent false-negatives. Of the 57 false-positives, close examination of the data revealed that 87.7% were while the participant was actually sitting, 87.7% were resolved by the activPAL within a mean of 60 s (SD = 117), and the remaining 12.3% were resolved because the participant changed posture (e.g., stood up) within of mean of 98 s (SD = 112) from the false sit-to-stand transition.

Hour-level analyses

All ActiGraph test measures overestimated the number of sit-to-stand transitions (see Table 2). The ActiGraph Hip, ActiGraph Hip-LF, and ActiGraph Wrist indicated 190%–202% more sit-to-stand transitions compared with direct observation. The ActiGraph Inclinometer indicated 258%–282% more sit-to-stand transitions compared with direct observation. ICC between the ActiGraph methods and the direct observation were all <0.12. The activPAL slightly overestimated sit-to-stand transitions compared with direct observation (6.84 vs 5.84 transitions per hour; 17% difference). The activPAL showed a modest correlation with direct observation (ICC = 0.26).

Hour-level agreement for measuring sit-to-stand transitions in youth with overweight and obesity (N = 16 h from 9 participants).

The Bland–Altman plots in Figure 1 show a slight pattern of greater overestimation as the number of sit-to-stand transitions increases for the ActiGraph HIP and ActiGraph Hip-LF, and a more substantial pattern of greater overestimation as the number of sit-to-stand transitions for the ActiGraph Wrist, and especially the ActiGraph Inclinometer. The 95% limits of agreement were generally large, indicating a wide range of error. ActivPAL, on the other hand, showed a much smaller mean difference and limits of agreement and showed fairly consistent bias as the number of sit-to-stand transitions increased.

Bland–Altman plots for hour-level agreement between ActiGraph/activPAL methods and direct observations. Lines are at zero, mean difference, 95% limits of agreement, and best fit.


The primary aims of the current study were to examine the validity of hip and wrist accelerometer cut points and the thigh-worn activPAL for measuring sit-to-stand transitions in youth as compared with direct observation. Few studies have investigated accelerometers for assessing sit-to-stand transitions (4,12,16,20,23), and limitations of these studies were that all were in adults or in prescribed activities. The current study extended the previous literature by examining both epoch-level and hour-level validity of these measures in youth with overweight and obesity, a population frequently targeted for intervention (3). Overall, findings show that the commonly used hip- and wrist-worn accelerometer cut points do not have strong validity for measuring sit-stand transitions. ActivPAL, on the other hand, showed good validity for assessing sit-to-stand transitions, and this is in agreement with adult studies (12,20).

As compared with direct observation, the ActiGraph Hip, ActiGraph Hip-LF, and ActiGraph Wrist overestimated sit-to-stand transitions by about triple the transitions, highlighting the limitation of accelerometer cut point methods for measuring the core behavior (sit-to-stand transitions) that comprises bout pattern calculations such as minutes in prolonged sedentary bouts. The ActiGraph Inclinometer function on the hip accelerometer did not perform more favorably than the other ActiGraph methods. These findings are similar to those in adult samples that showed poor validity for using accelerometer cut points to assess sit-to-stand transitions (4,16,20,23).

Several studies have shown that hip-worn accelerometer cut points are acceptable for assessing total sedentary time in youth (e.g., 19,26,27). However, present findings suggest using caution when considering using accelerometer cut points for assessing sit-to-stand transitions and sedentary bout patterns. The lack of validity for the hip and wrist accelerometer cut points for assessing sit-to-stand transitions has several implications. Evidence is accumulating on the deleterious effects of few sit-to-stand transitions in youth (6,31). However, a majority of this evidence was derived from accelerometer cut points. More valid measures of sit-to-stand transitions (e.g., activPAL or other methods of processing hip-worn accelerometer data) would likely result in reduced measurement error, more power for detecting true associations, and a better understanding of how and why sedentary behavior has negative health implications.

Although the activPAL slightly overestimated sit-to-stand transitions, the magnitude of the differences was small. These differences were slightly greater than those observed in previously published studies (2,12,20), and close examination of the present data revealed that the disagreement primarily occurred during the first half of the data collection (i.e., physical activity sessions) and not during the classroom activity. Thus, it appears that misestimation of sit-to-stand transitions is more frequent during activity sessions, which are more common in youth than adults. Most of the false transitions were caught (i.e., the correct posture was detected) by the activPAL algorithms within 1.5 min. Furthermore, slight overestimation of sit-to-stand transitions during physical activity is not likely to impact estimates of prolonged sitting (e.g., usual bout duration, minutes in >30-min bouts) at the day and participant level, which are commonly investigated (e.g., 14,15,30,31).

Other methods have evidence of validity or show promise for assessing sit-to-stand transitions. For example, wearing the ActiGraph on the thigh, similar to how the activPAL is worn, has evidence of validity for assessing posture (33), and consumer devices such as the LUMOback (Lumo Bodytech, Mountain View, CA) have evidence of validity for assessing total sedentary time (29). Other methods for processing accelerometer data, such as machine learning techniques that use the triaxial information from the accelerometer, have good evidence of validity for assessing energy expenditure and sedentary time, show promise for improving assessment of sit-to-stand transitions (21), and should be a priority of future research. Different methods, or even devices, may need to be used to validly assess different activities (e.g., physical activity vs sedentary time vs sit-to-stand transitions), although to minimize participant and researcher burden it would be valuable to use only one device. It is possible that other accelerometer cut points have better validity for assessing sit-to-stand transitions. However, some studies have suggested using higher cut points (17), whereas others suggest lower cut points (13), so there is no consensus. Changing the cut point is likely to trade off false-positives for false-negatives, so substantial improvement to overall validity is not likely to be observed simply from changing the cut point.

The strengths of this study include being among the first to investigate the validity of hip- and wrist-worn accelerometer cut points and of the activPAL activity monitor for assessing sit-to-stand transitions in free-living youth, examining both epoch- and hour-level metrics and using direct observation as the gold standard comparison measure. A limitation of the study is the small sample size and short period of observation per participant (i.e., 2 h), so findings may not generalize to other samples or populations. In addition, youth participants engaged in exercise for a portion of their time in the study. Although the exercises included typical activities that youth engage in on a regular basis (e.g., jumping, getting on floor and up again), these activities may have inflated the bias across measures. It is likely that agreement would have been higher if these activities were excluded, but this would limit external validity. Also, given evidence suggesting that weight status may affect the accuracy of accelerometry in adults (11), the current results may not be generalizable to normal weight youth. Although sedentary behavior is highly prevalent in free-living samples, the LF of sit-to-stand transitions in the present free-living sample made for a more robust test of negative predictive values (because there were so many nontransitions) and less robust test of positive predictive values. Thus, both negative and positive predictive values should be interpreted with caution, and all analyses should be taken into account when drawing conclusions.

Consistent with previous evidence in adults, there is little evidence to suggest that accelerometer cut point methods have acceptable validity for assessing sit-to-stand transitions in youth. These findings are not surprising because the hip and the wrist are not ideal placements for capturing posture, and similar acceleration counts can result from body movements occurring while sitting or standing. ActivPAL, on the other hand, has evidence of validity for assessing sit-to-stand transitions. Despite the common use of hip- and wrist-worn accelerometer cut point methods to assess sit-to-stand transitions and sedentary patterns, these methods should be used with caution. Use of these methods could lead to misestimation of associations between sedentary patterns and health. Future research should investigate other processing methods for hip- and wrist-worn accelerometer data and correction factors, and studies investigating sedentary patterns and health should consider including ActivPAL when possible.

The authors thank Ms. Jadlow and Pierre, Mr. Sanchez and Wheaton, and the PHIT Kids team for their support with this study.

There are no funding sources or conflicts of interest to report. The results of this study do not constitute endorsement by the American College of Sports Medicine. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.


1. ActiGraph Web site [Internet]. GT3X+ and wGT3X+ Device Manual. [cited 2016 July 28]. Available from:
2. Aminian S, Hinckson EA. Examining the validity of the activPAL monitor in measuring posture and ambulatory movement in children. Int J Behav Nutr Phys Act. 2012;9:119.
3. Azevedo LB, Ling J, Soos I, Robalino S, Ells L. The effectiveness of sedentary behaviour interventions for reducing body mass index in children and adolescents: systematic review and meta-analysis. Obes Rev. 2016;17(7):623–35.
4. Barreira TV, Zderic TW, Schuna JM Jr, Hamilton MT, Tudor-Locke C. Free-living activity counts-derived breaks in sedentary time: are they real transitions from sitting to standing? Gait Posture. 2015;42(1):70–2.
5. Baquet G, Stratton G, Van Praagh E, Berthoin S. Improving physical activity assessment in prepubertal children with high-frequency accelerometry monitoring: a methodological issue. Prev Med. 2007;44(2):143–7.
6. Carson V, Stone M, Faulkner G. Patterns of sedentary behavior and weight status among children. Pediatr Exerc Sci. 2014;26(1):95–102.
7. Carstensen B, Simpson J, Gurrin LC. Statistical models for assessing agreement in method comparison studies with replicate measurements. Int J Biostat. 2008;4(1):1–26.
8. Crouter SE, Flynn JI, Bassett DR Jr. Estimating physical activity in youth using a wrist accelerometer. Med Sci Sports Exerc. 2015;47(5):944–51.
9. Davies G, Reilly JJ, McGowan AJ, Dall PM, Granat MH, Paton JY. Validity, practical utility, and reliability of the activPAL in preschool children. Med Sci Sports Exerc. 2012;44(4):761–8.
10. Dunstan DW, Howard B, Healy GN, Owen N. Too much sitting—a health hazard. Diabetes Res Clin Pract. 2012;97(3):368–76.
11. Fieto Y, Bassett DR, Tyo B, Thompson DL. Effects of body mass index and tilt angle on output of two wearable activity monitors. Med Sci Sports Exerc. 2011;43(5):861–6.
12. Grant PM, Ryan CG, Tigbe WW, Granat MH. The validation of a novel activity monitor in the measurement of posture and motion during everyday activities. Br J Sports Med. 2006;40(12):992–7.
13. Hart TL, McClain JJ, Tudor-Locke C. Controlled and free-living evaluation of objective measures of sedentary and active behaviors. J Phys Act Health. 2011;8(6):848–57.
14. Healy GN, Clark BK, Winkler EA, Gardiner PA, Brown WJ, Matthews CE. Measurement of adults' sedentary time in population-based studies. Am J Prev Med. 2011;41(2):216–27.
15. Healy GN, Dunstan DW, Salmon J, et al. Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care. 2008;31(4):661–6.
16. Judice PB, Santos DA, Hamilton MT, Sardinha LB, Silva AM. Validity of GT3X and Actiheart to estimate sedentary time and breaks using activPAL as the reference in free-living conditions. Gait Posture. 2015;41(4):917–22.
17. Kozey-Keadle S, Libertine A, Lyden K, Staudenmayer J, Freedson PS. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc. 2011;43(8):1561–7.
18. Lee IM, Shiroma EJ. Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges. Br J Sports Med. 2014;48(3):197–201.
19. Lubans DR, Hesketh K, Cliff DP, et al. A systematic review of the validity and reliability of sedentary behaviour measures used with children and adolescents. Obes Rev. 2011;12(10):781–99.
20. Lyden K, Kozey Keadle SL, Staudenmayer JW, Freedson PS. Validity of two wearable monitors to estimate breaks from sedentary time. Med Sci Sports Exerc. 2012;44(11):2243–52.
21. Lyden K, Kozey Keadle S, Staudenmayer J, Freedson PS. A method to estimate free-living active and sedentary behavior from an accelerometer. Med Sci Sports Exerc. 2014;46(2):386–97.
22. Matthews CE, Hagströmer M, Pober DM, Bowles HR. Best practices for using physical activity monitors in population-based research. Med Sci Sports Exerc. 2012;44(1 Suppl):S68.
23. Montoye AHK, Pivarnik JM, Mudd LM, Biswas S, Pfeiffer KA. Validation and comparison of accelerometers worn on the hip, thigh, and wrists for measuring physical activity and sedentary behavior. AIMS Public Health. 2016;3(2):298–312.
24. Owen N, Healy GN, Matthews CE, Dunstan DW. Too much sitting: the population-health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38(3):105.
25. Pate RR, O'Neill JR, Lobelo F. The evolving definition of “sedentary.” Exerc Sport Sci Rev. 2008;36(4):173–8.
26. Pulsford RM, Cortina-Borja M, Rich C, Kinnafick FE, Dezateux C, Griffiths LJ. ActiGraph accelerometer-defined boundaries for sedentary behaviour and physical activity intensities in 7 year old children. PLoS One. 2011;6(8):e21822.
27. Ridgers ND, Salmon J, Ridley K, O'Connell E, Arundell L, Timperio A. Agreement between activPAL and ActiGraph for assessing children's sedentary time. Int J Behav Nutr Phys Act. 2012;9:15.
28. Ridley K, Ridgers ND, Salmon J. Criterion validity of the activPAL™ and ActiGraph for assessing children's sitting and standing time in a school classroom setting. Int J Behav Nutr Phys Act. 2016;13:75.
29. Rosenberger ME, Buman MP, Haskell WL, McConnell MV, Carstensen LL. Twenty-four hours of sleep, sedentary behavior, and physical activity with nine wearable devices. Med Sci Sports Exerc. 2016;48(3):457–65.
30. Sardinha LB, Andersen LB, Anderssen SA, et al. Objectively measured time spent sedentary is associated with insulin resistance independent of overall and central body fat in 9-to 10-year-old Portuguese children. Diabetes Care. 2008;31(3):569–75.
31. Saunders TJ, Tremblay MS, Mathieu ME, et al. Associations of sedentary behavior, sedentary bouts and breaks in sedentary time with cardiometabolic risk in children with a family history of obesity. PLoS One. 2013;8(11):e79143.
32. Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours.” Appl Physiol Nutr Metab. 2012;37(3):540–2.
33. Skotte J, Korshøj M, Kristiansen J, Hanisch C, Holtermann A. Detection of physical activity types using triaxial accelerometers. J Phys Act Health. 2014;11(1):76–84.
34. Tremblay MS, LeBlanc AG, Kho ME, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act. 2011;8:98.
35. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc. 2011;43(7):1360–8.
36. Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(11):S531–43.


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