scholarly journals Deriving An Ecologically Valid Accelerometer Cut-point For Free-living Physical Activity In Children: An Exploratory Study

2009 ◽  
Vol 41 ◽  
pp. 172
Author(s):  
Susan Bock ◽  
Christine Steel ◽  
Sally McLure ◽  
Helen Moore ◽  
Daniel Cooley ◽  
...  
2017 ◽  
Vol 29 (2) ◽  
pp. 268-277 ◽  
Author(s):  
Sofiya Alhassan ◽  
John R. Sirard ◽  
Laura B. F. Kurdziel ◽  
Samantha Merrigan ◽  
Cory Greever ◽  
...  

Purpose:The purpose of this study was to cross-validate previously developed Actiwatch (AW; Ekblom et al. 2012) and AcitGraph (AG; Sirard et al. 2005; AG-P, Pate et al. 2006) cut-point equations to categorize free-living physical activity (PA) of preschoolers using direct observation (DO) as the criterion measure. A secondary aim was to compare output from the AW and the AG from previously developed equations.Methods:Participants’ (n = 33; age = 4.4 ± 0.8 yrs; females, n=12) PA was directly observed for three 10-min periods during the preschool-day while wearing the AW (nondominant wrist) and AG (waist). Device specific cut-points were used to reduce the AW-E (Ekblom et al. 2012) and AG (AG-S, Sirard et al. 2005; AG-P, Pate et al. 2006) data into intensity categories. Spearman correlations (rsp) and agreement statistics were used to assess associations between the DO intensity categories and device data. Mixed model regression was used to identify differences in times spent in activity intensity categories.Results:There was a significant correlation between AW and AG output across all data (rsp = 0.41, p < .0001) and both were associated with the DO intensity categories (AW: rsp = 0.47, AG: rsp = 0.47; p < .001). At the individual level, all devices demonstrated relatively low sensitivity but higher specificity. At the group level, AW-E and AG-P provided similar estimates of time spent in moderate-to-vigorous PA (MVPA, AW-E: 4.7 ± 4.1, AG-P: 4.4 ± 3.3), compared with DO (5.1 ± 3.5). Conclusion: The AW-E and AG-P estimated times spent in MVPA were similar to DO, but the weak agreement statistics indicate that neither device cut-point equations provided accurate estimates at the individual level.


2007 ◽  
Vol 39 (Supplement) ◽  
pp. S185 ◽  
Author(s):  
Daniel P. Heil ◽  
Melicia Whitt-Glover ◽  
Peter H. Brubaker ◽  
Yukari Mori

2013 ◽  
Vol 10 (7) ◽  
pp. 1068-1074 ◽  
Author(s):  
M. Renée Umstattd Meyer ◽  
Stephanie L. Baller ◽  
Shawn M. Mitchell ◽  
Stewart G. Trost

Background:Accelerometers have become one of the most common methods of measuring physical activity (PA). Thus, validity of accelerometer data reduction approaches remains an important research area. Yet, few studies directly compare data reduction approaches and other PA measures in free-living samples.Objective:To compare PA estimates provided by 3 accelerometer data reduction approaches, steps, and 2 self-reported estimates: Crouter’s 2-regression model, Crouter’s refined 2-regression model, the weighted cut-point method adopted in the National Health and Nutrition Examination Survey (NHANES; 2003–2004 and 2005–2006 cycles), steps, IPAQ, and 7-day PA recall.Methods:A worksite sample (N = 87) completed online-surveys and wore ActiGraph GT1M accelerometers and pedometers (SW-200) during waking hours for 7 consecutive days. Daily time spent in sedentary, light, moderate, and vigorous intensity activity and percentage of participants meeting PA recommendations were calculated and compared.Results:Crouter’s 2-regression (161.8 ± 52.3 minutes/day) and refined 2-regression (137.6 ± 40.3 minutes/day) models provided significantly higher estimates of moderate and vigorous PA and proportions of those meeting PA recommendations (91% and 92%, respectively) as compared with the NHANES weighted cut-point method (39.5 ± 20.2 minutes/day, 18%). Differences between other measures were also significant.Conclusions:When comparing 3 accelerometer cut-point methods, steps, and self-report measures, estimates of PA participation vary substantially.


2010 ◽  
Vol 7 (4) ◽  
pp. 1558-1576 ◽  
Author(s):  
Roman Cuberek ◽  
Walid El Ansari ◽  
Karel Frömel ◽  
Krzysztof Skalik ◽  
Erik Sigmund

2018 ◽  
Vol 26 (2) ◽  
pp. 254-258 ◽  
Author(s):  
Giovanni Mario Pes ◽  
Maria Pina Dore ◽  
Alessandra Errigo ◽  
Michel Poulain

2011 ◽  
Vol 74 (11) ◽  
pp. 509-516 ◽  
Author(s):  
Clare Hocking ◽  
Juanita Murphy ◽  
Kirk Reed

Aim: This exploratory study aimed to uncover the strategies that older adults employ to ameliorate the impact of impairments and barriers to participation. Method: Eight participants were interviewed in their own homes, in a town or city in New Zealand. Findings: Inductive analysis of data revealed four main categories of strategies: strategies to keep safe, to recruit and accept help, to meet social and biological needs (nutritional and medical), and to conserve financial, material and bodily resources. Discussion: The study supports some previous findings of strategies used by older people, and demonstrates that enquiring into the strategies that older people devise and adopt into their own lives is a productive line of inquiry. The strategies described differ from those that occupational therapists recommend, and do not incorporate public health messages about the benefits of physical activity or recommendations about falls prevention. Conclusion: The findings suggest that asking older clients about the strategies that they use will uncover valuable information for therapists giving advice or issuing equipment to help older adults to manage in the community.


1982 ◽  
Vol 16 (3) ◽  
pp. 240-243
Author(s):  
Wayne T. Corbett ◽  
Harry M. Schey ◽  
A. W. Green

The mean and standard deviation over 24 h for 3 groups of animals - active, intermediate and inactive - in physical activity units were 10948 ± 3360, 2611 ± 1973 and 484 ± 316 respectively. The differences were significant ( P = 0·004), demonstrating the ability of the method to distinguish between groups that can be visibly differentiated. The small within-animal physical activity standard deviation (18·85 PAU) obtained in another group, suggests that it also yields reliable physical activity measurements for non-human primates. The monitoring device used can discriminate between individual nonhuman primate physical activity levels in a free-living environment and does not alter daily behaviour. This makes possible the study of the relationship between physical activity and atherosclerosis in nonhuman primates.


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