Search Results

You are looking at 1 - 4 of 4 items for

  • Author: Kelly A. Mackintosh x
Clear All Modify Search
Open access

Kelly A. Mackintosh, Kate Ridley, Gareth Stratton and Nicola D. Ridgers


This study sought to ascertain the energy expenditure (EE) associated with different sedentary and physically active free-play activities in primary school-aged children.


Twenty-eight children (13 boys; 11.4 ± 0.3 years; 1.45 ± 0.09 m; 20.0 ± 4.7 kg·m-2) from 1 primary school in Northwest England engaged in 6 activities representative of children’s play for 10 minutes (drawing, watching a DVD, playground games and free-choice) and 5 minutes (self-paced walking and jogging), with 5 minutes rest between each activity. Gas exchange variables were measured throughout. Resting energy expenditure was measured during 15 minutes of supine rest.


Child (Schofield-predicted) MET values for watching a DVD, self-paced jogging and playing reaction ball were significantly higher for girls (P < .05).


Utilizing a field-based protocol to examine children’s free-living behaviors, these data contribute to the scarcity of information concerning children’s EE during play to update the Compendium of Energy Expenditures for Youth.

Restricted access

Alexander H.K. Montoye, Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry and Karin A. Pfeiffer

Background: Machine learning may improve energy expenditure (EE) prediction from body-worn accelerometers. However, machine learning models are rarely cross-validated in an independent sample, and the use of machine learning raises additional questions including the effect of accelerometer placement and data type (count vs. raw) for optimal EE prediction. Purpose: To assess the accuracy of artificial neural network (ANN) models for EE prediction in youth using count-based or raw data from accelerometers worn on the hip, wrist, or in combination, and compare these to count-based, EE regression equations. Methods: Data were collected in two settings; one (n = 27) to calibrate the EE prediction models, and the other (n = 34) for model cross-validation. Participants wore a portable metabolic analyzer (EE criterion) and accelerometers on the left wrist and right hip while completing 30 minutes of exergames (calibration, cross-validation) and a maximal exercise test (calibration only). Six ANNs were created from the calibration data, separately by accelerometer placement (hip, wrist, combination) and data format (count-based, raw) to predict EE (15-second epochs). Three count-based linear regression equations were also developed for comparison to the ANNs. Results: The count-based, hip ANN demonstrated lower error (RMSE: 1.2 METs) than all other ANNs (RMSE: 1.7–3.6 METs) and EE regression equations (RMSE: 1.5–3.2 METs). However, all models showed bias toward the mean. Conclusion: An ANN developed for hip-worn accelerometers had higher accuracy for EE prediction during an exergame session than wrist or combination ANNs, and ANNs developed using count-based data had higher accuracy than ANNs developed using raw data.

Restricted access

Kelly A. Mackintosh, Nicola D. Ridgers, Rachel E. Evans and Melitta A. McNarry

Background: Regular physical activity (PA) is increasingly recognized as important in the care of patients with cystic fibrosis (CF), but there is a dearth of evidence regarding physical activity levels or how these are accrued in those with CF. Methods: PA was measured by a hip-worn accelerometer for 7 consecutive days in 18 children [10 boys; 12.4 (2.8) y] with mild to moderate CF and 18 age- and sex-matched controls [10 boys; 12.5 (2.7) y]. Results: Both children with CF and healthy children demonstrated similar physical activity levels and patterns of accumulation across the intensity spectrum, with higher levels of PA during weekdays in both groups. Forced expiratory volume in 1 second was predicted by high light PA in children with CF compared with low light PA in healthy children. Conclusion: These findings highlight weekends and light PA as areas warranting further research for the development of effective intervention strategies to increase PA in the youth CF population.

Restricted access

Rebecca M. Dagger, Ian G. Davies, Kelly A. Mackintosh, Genevieve L. Stone, Keith P. George, Stuart J. Fairclough and Lynne M. Boddy

Purpose: To assess the effects of the Children’s Health, Activity and Nutrition: Get Educated! intervention on body size, body composition, and peak oxygen uptake in a subsample of 10- to 11-year-old children. Methods: Sixty children were recruited from 12 schools (N = 6 intervention) to take part in the CHANGE! subsample study. Baseline, postintervention, and follow-up measures were completed in October 2010, March–April 2011, and June–July 2011, respectively. Outcome measures were body mass index z score, waist circumference, body composition assessed using dual-energy X-ray absorptiometry (baseline and follow-up only), and peak oxygen uptake. Results: Significant differences in mean trunk fat mass (control = 4.72 kg, intervention = 3.11 kg, P = .041) and trunk fat % (control = 23.08%, intervention = 17.75%, P = .022) between groups were observed at follow-up. Significant differences in waist circumference change scores from baseline to follow-up were observed between groups (control = 1.3 cm, intervention = −0.2 cm, P = .023). Favorable changes in body composition were observed in the intervention group; however, none of these changes reached statistical significance. No significant differences in peak oxygen uptake were observed. Conclusions: The results of the present study suggest the multicomponent curriculum intervention had small to medium beneficial effects on body size and composition health outcomes.