Establishing Wrist-Based Cutpoints for the Actical Accelerometer in Elementary School-Aged Children

Schaefer, Christine; Nace, Heidi J.; Browning, Ray

doi:10.1123/jpah.2011-0411

Cited by 16 publications

(14 citation statements)

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“…The choice of which cut-points to use has implications for PA researchers looking to quantify MVPA or estimate the prevalence of those meeting the PA recommendation. Our study is in agreement with Schaefer et al (17) who used ROC and regression analyses to establish PA cut-points for the wrist using the Actical accelerometer. They then used a free-living sample to estimate the minutes of MPA and VPA and percent of participants meeting the PA recommendation of 60 minutes per day using both methods.…”

Section: Discussionsupporting

confidence: 92%

“…However, only a few studies have addressed the wrist location. Specifically, the validity of algorithms for the Actical (1-axis; wrist) (7,17), Actiwatch (1-axis; wrist) (4), and GENEActiv (3-axis; wrist) (5,18) have been examined in the laboratory for children. Ekblom et al (4) showed that the Actiwatch accelerometer counts were significant correlated with EE (r = 0.80, p < 0.001) and Schaefer et al (18) demonstrated the ability of the GENEActiv to accurately classify SB and PA intensities.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Physical Activity in Youth Using a Wrist Accelerometer

Crouter

Flynn²,

Bassett³

2015

Medicine &Amp; Science in Sports &Amp; Exercise

116

149

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PURPOSE The purpose of this study was to develop and validate methods for analyzing wrist accelerometer data in youth. METHODS 181 youth (mean±SD; age, 12.0±1.5 yrs) completed 30-min of supine rest and 8-min each of 2 to 7 structured activities (selected from a list of 25). Receiver Operator Characteristic (ROC) curves and regression analyses were used to develop prediction equations for energy expenditure (child-METs; measured activity VO2 divided by measured resting VO2) and cut-points for computing time spent in sedentary behaviors (SB), light (LPA), moderate (MPA), and vigorous (VPA) physical activity. Both vertical axis (VA) and vector magnitude (VM) counts per 5 seconds were used for this purpose. The validation study included 42 youth (age, 12.6±0.8 yrs) who completed approximately 2-hrs of unstructured PA. During all measurements, activity data were collected using an ActiGraph GT3X or GT3X+, positioned on the dominant wrist. Oxygen consumption was measured using a Cosmed K4b2. Repeated measures ANOVAs were used to compare measured vs predicted child-METs (regression only), and time spent in SB, LPA, MPA, and VPA. RESULTS All ROC cut-points were similar for area under the curve (≥0.825), sensitivity (≥0.756), and specificity (≥0.634) and they significantly underestimated LPA and overestimated VPA (P<0.05). The VA and VM regression models were within ±0.21 child-METs of mean measured child-METs and ±2.5 minutes of measured time spent in SB, LPA, MPA, and VPA, respectively (P>0.05). CONCLUSION Compared to measured values, the VA and VM regression models developed on wrist accelerometer data had insignificant mean bias for child-METs and time spent in SB, LPA, MPA, and VPA; however they had large individual errors.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Estimating Physical Activity in Youth Using a Wrist Accelerometer

Crouter

Flynn²,

Bassett³

2015

Medicine &Amp; Science in Sports &Amp; Exercise

116

149

View full text Add to dashboard Cite

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“…In addition to development of tri-axial monitors, newer devices have been validated for wear on the wrist (61) , improving acceptability. Cut-points have been validated for MVPA for children (62) but data from hip-worn accelerometers should not be compared with wrist-worn accelerometers (63) , with discrimination between activity intensities better when worn on the hip rather than on the wrist (64) . Newer devices can output raw acceleration signals, offering promise for the future not only for quantification of physical activity but also pattern recognition.…”

Section: Recent Advances and Future Prospectsmentioning

confidence: 99%

Measuring physical activity in children and adolescents for dietary surveys: practicalities, problems and pitfalls

2014

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Physical inactivity is an important risk factor for many chronic diseases and contributes to obesity and poor mental well-being. The present paper describes the main advantages and disadvantages, practical problems, suggested uses, and future developments regarding self-reported and objective data collection in the context of dietary surveys. In dietary surveys, physical activity is measured primarily to estimate energy expenditure. Energy expenditure surveillance is important for tracking changes over time, particularly given the debates over the role of the relative importance of energy intake and expenditure changes in the aetiology of obesity. It is also important to assess the extent of underreporting of dietary intake in these surveys. Physical activity data collected should include details on the frequency, duration and relative intensity of activity for each activity type that contributes considerably to overall activity and energy expenditure. Problems of validity and reliability, associated with inaccurate assessment, recall bias, and social desirability bias, are well-known; children under 10 years cannot report their activities accurately. However, despite such limitations, questionnaires are still the dominant method of physical activity assessment in dietary surveys due to their low cost and relatively low participant burden. Objective, time-stamped measures that monitor heart rate and/or movement can provide more comprehensive, quantitative assessment of physical activity but at greater cost and participant burden. Although overcoming many limitations of questionnaires, objective measures also have drawbacks, including technical, practical and interpretational issues.

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“…Finally, compliance for the hip placement is lower in children, compared to adults, suggesting that other placements might be more feasible (Troiano et al, 2008). Recent studies have begun to examine the wrist more closely as an alternative placement in children (Schaefer, Nace, & Browning, 2013), overcoming concerns with data volume/integrity through 24-hour continuous data collection, however a major limitation to the wrist placement is the inability to capture activities involving locomotion. Finally, the ankle placement has not been extensively studied, yet may overcome many of the limitations of the hip site, through the opportunity for continuous, 24-hour data collection, and the limitations of the wrist site, through the ability to capture locomotion, a common form of physical activity.…”

mentioning

confidence: 99%

“…For the hip and wrist placements, these equations have been challenged and updated (Alhassan et al, 2012; R. C. Colley & Tremblay, 2011; Crouter et al, 2011; Hooker et al, 2012; Lyden, Kozey, Staudenmeyer, & Freedson, 2011; Rothney, Schaefer, Neumann, Choi, & Chen, 2008; Schaefer et al, 2013). The impetus for the current study was our laboratory’s experience applying the Actical manufacturer’s thresholds to ankle accelerometry data collected in free-living studies.…”

mentioning

confidence: 99%

Ankle Accelerometry for Assessing Physical Activity Among Adolescent Girls: Threshold Determination, Validity, Reliability, and Feasibility

Hager

Treuth

Gormely

et al. 2015

Research Quarterly for Exercise and Sport

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Purpose Ankle accelerometry allows for 24-hour data collection, improving data volume/integrity versus hip accelerometry. Using Actical ankle accelerometry, the purpose was to (a) develop sensitive/specific thresholds; (b) examine validity/reliability; (c) compare new thresholds with manufacturer’s; and (d) examine feasibility in a community sample (low-income, urban adolescent girls). Methods Two studies were conducted with 6th–7th grade girls (age 10–14 years): Laboratory study (n=24)- Two 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 (ICC): activity counts of 2 Acticals), criterion-related validity (correlation: activity counts and METs), and calculations of sensitivity, specificity, kappa and ROC curves for thresholds. Free-Living study (n=459)- an Actical was worn >7 days on the ankle (full 24-hour days retained). Analyses included feasibility (frequencies: missing data) and paired t-tests (new thresholds versus manufacturer’s). Results Laboratory study- 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. Free-Living study- 99.6% wore the accelerometer; 84.1% had complete/valid data (mean=5.7 days). Primary reasons for missing/invalid data: Improper programming/documentation (5.2%), failure to return device (5.0%), wear-time ≤2 days (2.8%). The Moderate to Vigorous Physical Activity (MVPA) threshold (>3200 counts/minute) yielded 37.2 minutes/day, 2–4.5 times lower than the manufacturer’s software (effect size=.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.

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Establishing Wrist-Based Cutpoints for the Actical Accelerometer in Elementary School-Aged Children

Cited by 16 publications

References 0 publications

Estimating Physical Activity in Youth Using a Wrist Accelerometer

Estimating Physical Activity in Youth Using a Wrist Accelerometer

Measuring physical activity in children and adolescents for dietary surveys: practicalities, problems and pitfalls

Ankle Accelerometry for Assessing Physical Activity Among Adolescent Girls: Threshold Determination, Validity, Reliability, and Feasibility

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