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1.
Accelerometry is the gold standard for field-based physical activity assessment in children; however, the plethora of devices, data reduction procedures, and cut-points available limits comparability between studies. This study aimed to compare physical activity variables from the ActiGraph GT3X+ and Actical accelerometers in children under free-living conditions. A cross-sectional study of 379 children aged 9–11 years from Ottawa (Canada) was conducted. Children wore the ActiGraph GT3X+ and Actical accelerometers on the hip simultaneously for 7 consecutive days (24-h protocol). Moderate-to-vigorous (MVPA), vigorous (VPA), moderate (MPA), and light (LPA) physical activity, as well as sedentary time, (SED) were derived using established data reduction protocols. Excellent agreement between devices was observed for MVPA (ICC = 0.73–0.80), with fair to good agreement for MPA, LPA and SED, and poor agreement for VPA. Bland-Altman plots showed excellent agreement for MVPA, LPA, and SED, adequate agreement for MPA, and poor agreement for VPA. MVPA derived from the Actical was 11.7% lower than the ActiGraph GT3X+. The ActiGraph GT3X+ and Actical are comparable for measuring children’s MVPA. However, comparison between devices for VPA, MPA, LPA, and SED are highly dependent on data reduction procedures and cut-points, and should be interpreted with caution.  相似文献   

2.
Wrist-based accelerometers are increasingly used to assess physical activity (PA) in population-based studies; however, cut-points to translate wrist-based accelerometer counts into PA intensity categories are still needed. The purpose of this study was to determine wrist-based cut-points for moderate- and vigorous-intensity ambulatory PA in adults for the Actical accelerometer. Healthy adults (n = 24) completed a four-phase treadmill exercise protocol (1.9, 3.0, 4.0 and 5.2 mph) while wearing an Actical accelerometer on their wrist. Metabolic equivalent of task (MET) levels were assessed by indirect calorimetry. Receiver operating characteristics (ROC) curves were generated to determine accelerometer counts that maximised sensitivity and specificity for classification of moderate (≥3 METs) and vigorous (>6 METs) ambulatory activity. The area under the ROC curves to discriminate moderate- and vigorous-intensity ambulatory activity were 0.93 (95% confidence interval [CI]: 0.90–0.97; P < 0.001) and 0.96 (95% CI: 0.94–0.99; P < 0.001), respectively. The identified cut-point for moderate-intensity ambulatory activity was 1031 counts per minute, which had a corresponding sensitivity and specificity of 85.6% and 87.5%, respectively. The identified cut-point for vigorous intensity ambulatory activity was 3589 counts per minute, which had a corresponding sensitivity and specificity of 88.0% and 98.7%, respectively. This study established intensity-specific cut-points for wrist-based wear of the Actical accelerometer which are recommended for quantification of moderate- and vigorous-intensity ambulatory activity.  相似文献   

3.
Accelerometry is increasingly used as a physical activity surveillance device that can quantify the amount of time spent moving at a range of intensities. This study proposes physical activity intensity cut-points for the Actical accelerometer. Thirty-eight volunteers completed a multi-stage treadmill protocol at 3, 5, and 8 km · h?1 (2, 3.3, and 8 METs) while wearing Actical accelerometers initialized to collect data in 60-s epochs. Using a decision boundary analytical approach, moderate and vigorous physical activity intensity cut-points were derived for the Actical accelerometer. In adults (n = 26), the cut-point for moderate physical activity intensity occurred at 1535 counts per minute and the vigorous cut-point occurred at 3960 counts per minute. In children (n = 12), the cut-point for moderate physical activity intensity occurred at 1600 counts per minute and the vigorous cut-point occurred at 4760 counts per minute. Improved classification of physical activity intensity using the decision boundary cut-points was observed compared with using mean values for each protocol stage. The cut-points derived are recommended for use in adults. The cut-points derived for children confirm the findings of previous studies.  相似文献   

4.
Calibration of two objective measures of physical activity for children   总被引：9，自引：7，他引：2  
A calibration study was conducted to determine the threshold counts for two commonly used accelerometers, the ActiGraph and the Actical, to classify activities by intensity in children 5 to 8 years of age. Thirty-three children wore both accelerometers and a COSMED portable metabolic system during 15 min of rest and then performed up to nine different activities for 7 min each, on two separate days in the laboratory. Oxygen consumption was measured on a breath-by-breath basis, and accelerometer data were collected in 15-s epochs. Using receiver operating characteristic curve (ROC) analysis, cutpoints that maximised both sensitivity and specificity were determined for sedentary, moderate and vigorous activities. For both accelerometers, discrimination of sedentary behaviour was almost perfect, with the area under the ROC curve at or exceeding 0.98. For both the ActiGraph and Actical, the discrimination of moderate (0.85 and 0.86, respectively) and vigorous activity (0.83 and 0.86, respectively) was acceptable, but not as precise as for sedentary behaviour. This calibration study, using indirect calorimetry, suggests that the two accelerometers can be used to distinguish differing levels of physical activity intensity as well as inactivity among children 5 to 8 years of age.  相似文献   

5.
Abstract

Accelerometry is increasingly used as a physical activity surveillance device that can quantify the amount of time spent moving at a range of intensities. This study proposes physical activity intensity cut-points for the Actical accelerometer. Thirty-eight volunteers completed a multi-stage treadmill protocol at 3, 5, and 8 km · h?1 (2, 3.3, and 8 METs) while wearing Actical accelerometers initialized to collect data in 60-s epochs. Using a decision boundary analytical approach, moderate and vigorous physical activity intensity cut-points were derived for the Actical accelerometer. In adults (n = 26), the cut-point for moderate physical activity intensity occurred at 1535 counts per minute and the vigorous cut-point occurred at 3960 counts per minute. In children (n = 12), the cut-point for moderate physical activity intensity occurred at 1600 counts per minute and the vigorous cut-point occurred at 4760 counts per minute. Improved classification of physical activity intensity using the decision boundary cut-points was observed compared with using mean values for each protocol stage. The cut-points derived are recommended for use in adults. The cut-points derived for children confirm the findings of previous studies.  相似文献   

6.
Improving sedentary measurement is critical to understanding sedentary-health associations in youth. This study assessed agreement between the thigh-worn activPAL and commonly used hip-worn ActiGraph accelerometer methods for assessing sedentary patterns in children. Both devices were worn by 8–12-year-olds (N = 195) for 4.6 ± 1.9 days. Two ActiGraph cut-points were applied to two epoch durations: ≤25 counts (c)/15 s, ≤75c/15s, ≤100c/60s, and ≤300c/60s. Bias, mean absolute deviation (MAD), and intraclass correlation coefficients (ICCs) tested agreement between devices for total sedentary time and 11 sedentary pattern variables (usual bout duration, sedentary time accumulated in various bout durations, breaks/day, break rate, and alpha). For most sedentary pattern variables, ActiGraph 25c/15s, 75c/15s, and 100c/60s had poor ICCs, with bias and MAD >20%. ActiGraph 300c/60s had a better agreement than the other cut-points, but all ICCs were <0.587. ActiGraph underestimated sedentary time in longer bouts and usual bout duration, and overestimated sedentary time in shorter bouts, breaks/day, and alpha. For total sedentary time, ActiGraph 25c/15s, 300c/60s, and 75c/15s had good/fair ICCs, with bias and MAD <20%. Sedentary patterns derived from two commonly used ActiGraph cut-points did not appear to reflect postural changes. These differences between measurement devices should be considered when interpreting findings from sedentary pattern studies.  相似文献   

7.
This study compared the energy expenditure (EE) levels during object projection skill performance (OPSP) as assessed by indirect calorimetry and accelerometry. Thirty-four adults (female n = 18) aged 18–30 (23.5 ± 2.5 years) performed three, 9-min sessions of kicking, over-arm throwing, and striking performed at 6-, 12-, and 30-sec intervals. EE was estimated (METS) using indirect calorimetry (COSMED K4b2) and hip-worn accelerometry (ActiGraph GT3X+). EE using indirect calorimetry demonstrated moderate-intensity physical activity (3.4 ± 0.7 METS––30-sec interval, 5.8 ± 1.2 METS––12-sec interval) to vigorous intensity physical activity (8.3 ± 1.7 METS––6-sec interval). However, accelerometry predicted EE suggested only light-intensity physical activity (1.7 ± 0.2 METS––30-sec interval, 2.2 ± 0.4 METS––12-sec interval, 2.7 ± 0.6 METS––6-sec interval). Hip-worn, ActiGraph GT3X+ accelerometers do not adequately capture physical activity intensity levels during OPSP, regardless of differences in skill performance intervals.  相似文献   

8.
ABSTRACT

Purpose: To compare children’s energy expenditure (EE) levels during object projection skill performance (OPSP; e.g., kicking, throwing, striking) as assessed by hip- and wrist-worn accelerometers. Method: Forty-two children (female n = 20, Mage = 8.1 ± 0.8 years) performed three, nine-minute sessions of kicking, over-arm throwing, and striking at performance intervals of 6, 12, and 30 seconds. EE was estimated using indirect calorimetry (COSMED k4b2) and accelerometers (ActiGraph GT3X+) worn on three different locations (hip, dominant-wrist, and non-dominant-wrist) using four commonly used cut-points. Bland-Altman plots were used to analyze the agreement in EE estimations between accelerometry and indirect calorimetry (METS). Chi-square goodness of fit tests were used to examine the agreement between accelerometry and indirect calorimetry. Results: Hip- and wrist-worn accelerometers underestimated EE, compared to indirect calorimetry, during all performance conditions. Skill practice at a rate of two trials per minute resulted in the equivalent of moderate PA and five trials per minute resulted in vigorous PA (as measured by indirect calorimetry), yet was only categorized as light and/or moderate activity by all measured forms of accelerometry. Conclusion: This is one of the first studies to evaluate the ability of hip- and wrist-worn accelerometers to predict PA intensity levels during OPSP in children. These data may significantly impact PA intervention measurement strategies by revealing the lack of validity in accelerometers to accurately predict PA levels during OPSP in children.  相似文献   

9.
This study assessed children’s physical activity (PA) levels derived from wrist-worn GENEActiv and hip-worn ActiGraph GT3X+ accelerometers and examined the comparability of PA levels between the two devices throughout the segmented week. One hundred and twenty-nine 9–10-year-old children (79 girls) wore a GENEActiv (GAwrist) and ActiGraph GT3X+ (AGhip) accelerometer on the left wrist and right hip, respectively, for 7 days. Mean minutes of light PA (LPA) and moderate-to-vigorous PA (MVPA) per weekday (whole-day, before-school, school and after-school) and weekend day (whole-day, morning and afternoon–evening) segments were calculated, and expressed as percentage of segment time. Repeated measures analysis of variance examined differences in LPA and MVPA between GAwrist and AGhip for each time segment. Bland–Altman plots assessed between-device agreement for LPA and MVPA for whole weekday and whole weekend day segments. Correlations between GAwrist and AGhip were weak for LPA (r = 0.18–0.28), but strong for MVPA (r = 0.80–0.86). LPA and MVPA levels during all weekday and weekend day segments were significantly higher for GAwrist than AGhip (p < 0.001). The largest inter-device percent difference of 26% was observed in LPA during the school day segment. Our data suggest that correction factors are needed to improve raw PA level comparability between GAwrist and AGhip.  相似文献   

10.
This study investigated the effects of epoch length and cut point selection on adolescent physical activity intensity quantification using vertical axis and vector magnitude (VM) measurement with the ActiGraph GT3X+ accelerometer. Four hundred and nine adolescents (211 males; 198 females) aged 12–16 years of age wore accelerometers during waking hours. The GT3X+ acceleration counts were reintegrated into 1, 5, 15, 30 and 60 s epoch lengths for both vertical axis and VM counts. One cut point was applied to vertical axis counts and three different cut points were applied to VM counts for each epoch length. Significant differences (P < 0.01) in mean total counts per day were observed between vertical axis and VM counts, and between epoch lengths for VM only. Differences in physical activity levels were observed between vertical and VM cut points, and between epoch lengths across all activity intensities. Our findings illustrate the magnitude of differences in physical activity outcomes that occur between axis measurement, cut points and epoch length. The magnitude of difference across epoch length must be considered in the interpretation of accelerometer data and seen as a confounding variable when comparing physical activity levels between studies.  相似文献   

11.
ABSTRACT

Average acceleration (AvAcc) and intensity gradient (IG) have been proposed as standardised metrics describing physical activity (PA) volume and intensity, respectively. We examined hypothesised between-group PA differences in AvAcc and IG, and their associations with health and well-being indicators in children. ActiGraph GT9X wrist accelerometers were worn for 24-h·d?1 over 7days by 145 children aged 9–10. Raw accelerations were averaged per 5-s epoch to represent AvAcc over 24-h. IG represented the relationship between log values for intensity and time. Moderate-to-vigorous PA (MVPA) was estimated using youth cutpoints. BMI z-scores, waist-to-height ratio (WHtR), peak oxygen uptake (VO2peak), Metabolic Syndrome risk (MetS score), and well-being were assessed cross-sectionally, and 8-weeks later. Hypothesised between-group differences were consistently observed for IG only (p < .001). AvAcc was strongly correlated with MVPA (r = 0.96), while moderate correlations were observed between IG and MVPA (r = 0.50) and AvAcc (r = 0.54). IG was significantly associated with health indicators, independent of AvAcc (p < .001). AvAcc was associated with well-being, independent of IG (p < .05). IG was significantly associated with WHtR (p < .01) and MetS score (p < .05) at 8-weeks follow-up. IG is sensitive as a gauge of PA intensity that is independent of total PA volume, and which relates to important health indicators in children.  相似文献   

12.
This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a metabolic analyzer (EE criterion). Visit 1 (V1) involved structured, 5-min activities dictated by researchers; Visit 2 (V2) allowed participants activity choice and duration (simulated free-living). EE prediction models were developed incorporating data from one setting (V1/V2; V2/V2) or both settings (V1V2/V2). The V1V2/V2 method had the lowest root mean square error (RMSE) for EE prediction (1.04–1.23 vs. 1.10–1.34 METs for V1/V2, V2/V2), and the ankle-worn accelerometer had the lowest RMSE of all accelerometers (1.04–1.18 vs. 1.17–1.34 METs for other placements). The ankle-worn accelerometer and associated EE prediction models developed using data from both structured and simulated free-living settings should be considered for optimal EE prediction accuracy.  相似文献   

13.
This study explored the validity of ActiGraph-determined sedentary time (<50 cpm, <100 cpm, <150 cpm, <200 cpm, <250 cpm) compared with the activPAL in a free-living sample of bus drivers. Twenty-eight participants were recruited between November 2013 and February 2014. Participants wore an activPAL3 and ActiGraph GT3X+ concurrently for 7 days and completed a daily diary. Time spent sedentary during waking hours on workdays, non-workdays, during working-hours, and non-working hours were compared between instruments. During working hours, all ActiGraph cut-points significantly underestimated sedentary time (p < 0.05), whereas during non-working hours the <50 cpm cut-point demonstrated the closest agreement (ActiGraph sedentary time: 250 ± 75 minutes versus activPAL sedentary time: 236 ± 65 minutes). Receiver operating characteristic analyses revealed that on workdays and non-workdays the ActiGraph cut-points exhibited relatively low sensitivity (all <0.62) and specificity (all <0.49) values. The use of the ActiGraph to measure sedentary time in this understudied, highly sedentary and at risk occupational group is not recommended.  相似文献   

14.
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15.
This study examined the validity of current Actical activity energy expenditure (AEE) equations and intensity cut-points in preschoolers using AEE and direct observation as criterion measures. Forty 4–6-year-olds (5.3 ± 1.0 years) completed a ~150-min room calorimeter protocol involving age-appropriate sedentary behaviours (SBs), light intensity physical activities (LPAs) and moderate-to-vigorous intensity physical activities (MVPAs). AEE and/or physical activity intensity were calculated using Actical equations and cut-points by Adolph, Evenson, Pfeiffer and Puyau. Predictive validity was examined using paired sample t-tests. Classification accuracy was evaluated using weighted kappas, sensitivity, specificity and area under the receiver operating characteristic curve. The Pfeiffer equation significantly overestimated AEE during SB and underestimated AEE during LPA (P < 0.0125 for both). There was no significant difference between measured and predicted AEEs during MVPA. The Adolph cut-point showed significantly higher accuracy for classifying SB, LPA and MVPA than all others. The available Actical equation does not provide accurate estimates of AEE across all intensities in preschoolers. However, the Pfeiffer equation performed reasonably well for MVPA. Using cut-points of ≤6 counts · 15 s?1, 7–286 counts · 15 s?1 and ≥ 287 counts · 15 s?1 when classifying SB, LPA and MVPA, respectively, is recommended.  相似文献   

16.
Background:Public health guidelines have called for innovative and flexible physical activity(PA)intervention strategies to promote PA and health amid the coronavirus disease 2019(COVID-19)pandemic.Therefore,this study’s purpose was to examine the effects of a home-based,YouTube-delivered PA intervention grounded in self-determination theory on young adults’free-living PA,sedentary behavior,and sleep quality(NCT04499547).Methods:Sixty-four young adults(48 females;age=22.8±3.4 years,mean±SD;body mass index=23.1±2.6 kg/m2)were randomized(1:1)into the intervention group,which received weekly aerobic and muscle-strengthening PA videos,or control group,which received weekly general health education videos,for 12 weeks.Our primary outcome was free-living moderate-to-vigorous PA(MVPA)and our secondary outcomes were sedentary behavior,light PA,and sleep quality(measured using ActiGraph accelerometers)along with muscle-strengthening PA frequency,self-determination theory-related motivation(non-regulation,external regulation,introjected regulation,identified regulation,integrated regulation,and intrinsic regulation),and perceived PA barriers(assessed using validated questionnaires).Repeated measures analysis of variances(ANOVAs)examined between-group differences at an adjusted significance level of 0.004 and effect sizes as partial eta-squared(η;).Results:We observed statistically significant interaction effects for MVPA,sleep efficiency,muscle-strengthening PA frequency,non-regulation,integrated regulation,intrinsic regulation,and perceived PA barriers(F(1,62)=10.75-77.67,p<0.001-0.002,ηp2=0.15-0.56)with all outcomes favoring the intervention group.We observed no statistically significant differences in either group for sedentary behavior,light PA,sleep duration,or external,introjected,and identified regulations after 12 weeks(F(1,62)=1.11-3.64,p=0.06-0.61).Conclusion:With national COVID-19 restrictions still in place and uncertainty regarding post-pandemic PA environments and behaviors,a remote,YouTube-delivered PA intervention may help foster clinically meaningful improvements in young adults’free-living MVPA,musclestrengthening PA frequency,sleep efficiency,PA-related intrinsic motivation,and perceived PA barriers.  相似文献   

17.
Purpose: Ankle accelerometry allows for 24-hr data collection and improves data volume/integrity versus hip accelerometry. Using Actical ankle accelerometry, the purpose of this study was to (a) develop sensitive/specific thresholds, (b) examine validity/reliability, (c) compare new thresholds with those of the manufacturer, and (d) examine feasibility in a community sample (low-income, urban adolescent girls). Method: Two studies were conducted with 6th- through 7th-grade girls (aged 10–14 years old): First was a laboratory study (n = 24), in which 2 Actical accelerometers were placed on the ankle and worn while measuring energy expenditure (Cosmed K4b2, metabolic equivalents [METs]) during 10 prescribed activities. Analyses included device equivalence reliability (intraclass correlation coefficient [ICC], activity counts of 2 Acticals), criterion-related validity (correlation, activity counts and METs), and calculations of sensitivity, specificity, kappa, and receiver-operating characteristic curves for thresholds. The second was a free-living study (n = 459), in which an Actical was worn for more than 7 days on the ankle (full 24-hr days retained). Analyses included feasibility (frequencies, missing data) and paired t tests (new thresholds vs. those of the manufacturer). Results: In the laboratory study, the Actical demonstrated reliability (ICC = .92) and validity (r = .81). Thresholds demonstrated sensitivity (91%), specificity (84%), kappa = .73 (p = .043), area under curve range = .81–.97. In the free-living study, 99.6% of participants wore the accelerometer; 84.1% had complete/valid data (mean = 5.7 days). Primary reasons for missing/invalid data included: improper programming/documentation (5.2%), failure to return device (5.0%), and wear-time ≤ 2 days (2.8%). The moderate-to-vigorous physical activity threshold (> 3,200 counts/minute) yielded 37.2 min/day, 2 to 4.5 times lower than that of the manufacturer's software (effect size = 0.74–4.05). Conclusions: Validity, reliability, and feasibility evidences support Actical ankle accelerometry to assess physical activity in community studies of adolescent girls. When comparing manufacturers' software versus new thresholds, a major difference was observed.  相似文献   

18.
This study aimed to validate the Sedentary Sphere posture classification method from wrist-worn accelerometers in children. Twenty-seven 9–10-year-old children wore ActiGraph GT9X (AG) and GENEActiv (GA) accelerometers on both wrists, and activPAL on the thigh while completing prescribed activities: five sedentary activities, standing with a phone, walking (criterion for all 7: observation) and 10-min free-living play (criterion: activPAL). In an independent sample, 21 children wore AG and GA accelerometers on the non-dominant wrist and activPAL for two days of free-living. Per cent accuracy, pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC) analyses were completed. Accuracy was similar, for prescribed activities irrespective of brand (non-dominant wrist: 77–78%; dominant wrist: 79%). Posture estimates were equivalent between wrists within brand (±6%, ICC > 0.81, lower 95% CI ≥ 0.75), between brands worn on the same wrist (±5%, ICC ≥ 0.84, lower 95% CI ≥ 0.80) and between brands worn on opposing wrists (±6%, ICC ≥ 0.78, lower 95% CI ≥ 0.72). Agreement with activPAL during free-living was 77%, but sedentary time was underestimated by 7% (GA) and 10% (AG). The Sedentary Sphere can be used to classify posture from wrist-worn AG and GA accelerometers for group-level estimates in children, but future work is needed to improve the algorithm for better individual-level results.  相似文献   

19.
Abstract

The high prevalence of insufficient physical activity (PA) among adolescents is an important public health issue. Studying reasons for disliking PA might help researchers better understand its underlying mechanisms, yet this psychological construct has been understudied. This study established the psychometric properties of the German language version of the Girls' Disinclination for Physical Activity Scale (G-DAS-Ger). Data were collected on a sample of 257 adolescent girls in Austria (mean age: 13.0 ± 0.7 years) using the G-DAS-Ger and the Physical Activity Enjoyment Scale. One week after the first assessment, the questionnaires were re-administered to 78 girls. Between two administrations, PA of 215 girls was monitored for seven consecutive days using the ActiGraph GT3X+ accelerometers. Confirmatory factor analysis of G-DAS-Ger showed good fit for a three-factor model (χ2/df = 2.025; Bollen–Stine (B-S) p = 0.159; root mean square error of approximation = 0.063; standardised root mean square residual = 0.054; comparative fit index = 0.950). Cronbach's alphas for G-DAS-Ger factors/subscales ranged 0.64–0.76. The test–retest reliability assessed by Spearman's rank correlation ranged 0.62–0.75. Only one subscale correlated significantly with vigorous-intensity PA (Spearman's rho = ?0.16) and none with moderate-to-vigorous-intensity PA, which indicated poor predictive validity of the G-DAS-Ger. Correlations between G-DAS-Ger subscales and enjoyment of PA ranged from ?0.29 to ?0.41, indicating satisfactory convergent validity. The G-DAS-Ger may be used in its present form to assess disinclination for PA among adolescent girls in German-speaking countries. However, our results put into question the stability of the originally proposed factor structure of the questionnaire and its predictive validity among German-speaking adolescent girls. Methodological refinements to the G-DAS-Ger may be required to improve its psychometric properties in this population.  相似文献   

20.
This study aimed at translating the physical activity (PA) guideline (180 min of total PA per day) into a step count target in preschoolers. 535 Flemish preschoolers (mean age: 4.41 ± 0.58) wore an ActiGraph accelerometer (GT1M, GT3X and GT3X+) – with activated step count function – for four consecutive days. The step count target was calculated from the accelerometer output using a regression equation, applying four different cut-points for light-to-vigorous PA: Pate, Evenson, Reilly, and Van Cauwenberghe. The present analysis showed that 180 min of total PA per day is equivalent to the following step count targets: 5,274 steps/day using the Pate cut-point, 4,653 steps/day using the Evenson cut-point, 11,379 steps/day using the Reilly cut-point and 13,326 steps/day using the Van Cauwenberghe cut-point. Future studies should focus on achieving consensus on which cut-points to use in preschoolers before a definite step count target in preschoolers can be proposed. Until then, we propose to use a provisional step count target of 11,500 steps/day as this step count target is attainable, realistic and helpful in promoting preschoolers’ PA.  相似文献   

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