Effects of a physical education intervention on cognitive function in young children: randomized controlled pilot study

Randomized controlled trials (RCT) are required to test relationships between physical activity and cognition in children, but these must be informed by exploratory studies. This study aimed to inform future RCT by: conducting practical utility and reliability studies to identify appropriate cognitive outcome measures; piloting an RCT of a 10 week physical education (PE) intervention which involved 2hours per week of aerobically intense PE compared to 2 hours of standard PE (control). 64 healthy children (mean age 6.2 yrs SD 0.3; 33 boys) recruited from 6 primary schools. Outcome measures were the Cambridge Neuropsychological Test Battery (CANTAB), the Attention Network Test (ANT), the Cognitive Assessment System (CAS) and the short form of the Connor’s Parent Rating Scale (CPRS:S). Physical activity was measured habitually and during PE sessions using the Actigraph accelerometer. Test- retest intraclass correlations from CANTAB Spatial Span (r 0.51) and Spatial Working Memory Errors (0.59) and ANT Reaction Time (0.37) and ANT Accuracy (0.60) were significant, but low. Physical activity was significantly higher during intervention vs. control PE sessions (p <0.0001). There were no significant differences between intervention and control group changes in CAS scores. Differences between intervention and control groups favoring the intervention were observed for CANTAB Spatial Span, CANTAB Spatial Working Memory Errors, and ANT Accuracy. The present study has identified practical and age-appropriate cognitive and behavioral outcome measures for future RCT, and identified that schools are willing to increase PE time

A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity

Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96 % with 95 % for sensitivity, 99 % for specificity and a kappa value of 94 % when three and four feature combinations were used

Results of soy-based meal replacement formula on weight, anthropometry, serum lipids & blood pressure during a 40-week clinical weight loss trial

BACKGROUND: To evaluate the intermediate-term health outcomes associated with a soy-based meal replacement, and to compare the weight loss efficacy of two distinct patterns of caloric restriction. METHODS: Ninety overweight/obese (28 < BMI ≤ 41 kg/m(2)) adults received a single session of dietary counseling and were randomized to either 12 weeks at 1200 kcal/day, 16 weeks at 1500 kcal/d and 12 weeks at 1800 kcal/d (i.e., the 12/15/18 diet group), or 28 weeks at 1500 kcal/d and 12 weeks at 1800 kcal/d (i.e., the 15/18 diet group). Weight, body fat, waist circumference, blood pressure and serum lipid concentrations were measured at 4-week intervals throughout the 40-week trial. RESULTS: Subjects in both treatments showed statistically significant improvements in outcomes. A regression model for weight change suggests that subjects with larger baseline weights tended to lose more weight and subjects in the 12/15/18 group tended to experience, on average, an additional 0.9 kg of weight loss compared with subjects in the 15/18 group. CONCLUSION: Both treatments using the soy-based meal replacement program were associated with significant and comparable weight loss and improvements on selected health variables

Developing self-regulation for dietary temptations: intervention effects on physical, self-regulatory and psychological outcomes

We aimed to investigate whether a self-regulatory skills intervention can improve weight loss-related outcomes. Fifty-five participants (M BMI = 32.60 ± 4.86) were randomized into self-regulation training and advice groups and received two training workshops and weekly practice tasks. The self-regulation training group was trained to use six self-regulatory skills: Delayed gratification, thought control, goal setting, self-monitoring, mindfulness, and coping. The advice group received dietary and physical activity advice for weight loss. Physical, self-regulatory, and psychological measures were taken at baseline, end of intervention (week 8) and at follow-up (week 12). Using intention-to-treat analysis, weight, waist circumference, body fat and body mass index (BMI) were significantly reduced at follow-up for both groups. There were significant increases in all six self-regulatory skills and the psychological measures of self-efficacy, self-regulatory success, and physical self-worth for both groups. Results indicate that self-regulatory skills training might be as effective as dietary and physical activity advice in terms of weight loss and related outcomes

A calibration protocol for population-specific accelerometer cut-points in children

PurposeTo test a field-based protocol using intermittent activities representative of children\u27s physical activity behaviours, to generate behaviourally valid, population-specific accelerometer cut-points for sedentary behaviour, moderate, and vigorous physical activity.MethodsTwenty-eight children (46% boys) aged 10&ndash;11 years wore a hip-mounted uniaxial GT1M ActiGraph and engaged in 6 activities representative of children\u27s play. A validated direct observation protocol was used as the criterion measure of physical activity. Receiver Operating Characteristics (ROC) curve analyses were conducted with four semi-structured activities to determine the accelerometer cut-points. To examine classification differences, cut-points were cross-validated with free-play and DVD viewing activities.ResultsCut-points of &le;372, &gt;2160 and &gt;4806 counts&bull;min&minus;1 representing sedentary, moderate and vigorous intensity thresholds, respectively, provided the optimal balance between the related needs for sensitivity (accurately detecting activity) and specificity (limiting misclassification of the activity). Cross-validation data demonstrated that these values yielded the best overall kappa scores (0.97; 0.71; 0.62), and a high classification agreement (98.6%; 89.0%; 87.2%), respectively. Specificity values of 96&ndash;97% showed that the developed cut-points accurately detected physical activity, and sensitivity values (89&ndash;99%) indicated that minutes of activity were seldom incorrectly classified as inactivity.ConclusionThe development of an inexpensive and replicable field-based protocol to generate behaviourally valid and population-specific accelerometer cut-points may improve the classification of physical activity levels in children, which could enhance subsequent intervention and observational studies.<br /

The effect of a curriculum-based physical activity intervention on accelerometer-assessed physical activity in schoolchildren: a non-randomised mixed methods controlled before-and-after study

Classroom-based physical activity (PA) interventions offer the opportunity to increase PA without disrupting the curriculum. We aimed to explore the feasibility and potential effectiveness of a classroom-based intervention on moderate to vigorous PA (MVPA) and total PA. The secondary aim was to assess the acceptability and sustainability of the intervention. In a mixed-methods, non-randomised, exploratory controlled before-and-after study, 152 children (10 ± 0.7 years) were recruited from five schools; two intervention (n = 72) and three control (n = 80) schools. School teachers delivered an 8-week classroom-based intervention, comprising of 10 minutes daily MVPA integrated into the curriculum. The control schools maintained their usual school routine. Mean daily MVPA (min), total PA (mean cpm), physical fitness, and health-related quality of life measurements were taken at baseline, end of intervention, and 4-weeks post-intervention (follow-up). Data were analysed using a constrained baseline longitudinal analysis model accounting for the hierarchical data structure. For the primary outcomes (MVPA and total PA) the posterior mean difference and 95% compatibility interval were derived using a semi-Bayesian approach with an explicit prior. The acceptability and sustainability of the intervention was explored via thematic content analysis of focus group discussions with teachers (n = 5) and children (n = 50). The difference in mean daily MVPA (intervention-control) was 2.8 (-12.5 to 18.0) min/day at 8 weeks and 7.0 (-8.8 to 22.8) min/day at follow-up. For total PA, the differences were -2 (-127 to 124) cpm at 8-weeks and 11 (-121 to 143) cpm at follow-up. The interval estimates indicate that meaningful mean effects (both positive and negative) as well as trivial effects are reasonably compatible with the data and design. The intervention was received positively with continuation reported by the teachers and children. Classroom-based PA could hold promise for increasing average daily MVPA, but a large cluster randomised controlled trial is required

Predicting complete loss to follow-up after a health-education program: number of absences and face-to-face contact with a researcher

<p>Abstract</p> <p>Background</p> <p>Research on health-education programs requires longitudinal data. Loss to follow-up can lead to imprecision and bias, and <it>complete </it>loss to follow-up is particularly damaging. If that loss is predictable, then efforts to prevent it can be focused on those program participants who are at the highest risk. We identified predictors of complete loss to follow-up in a longitudinal cohort study.</p> <p>Methods</p> <p>Data were collected over 1 year in a study of adults with chronic illnesses who were in a program to learn self-management skills. Following baseline measurements, the program had one group-discussion session each week for six weeks. Follow-up questionnaires were sent 3, 6, and 12 months after the baseline measurement. A person was classified as completely lost to follow-up if none of those three follow-up questionnaires had been returned by two months after the last one was sent.</p> <p>We tested two hypotheses: that complete loss to follow-up was directly associated with the number of absences from the program sessions, and that it was less common among people who had had face-to-face contact with one of the researchers. We also tested predictors of data loss identified previously and examined associations with specific diagnoses.</p> <p>Using the unpaired t-test, the U test, Fisher's exact test, and logistic regression, we identified good predictors of complete loss to follow-up.</p> <p>Results</p> <p>The prevalence of complete loss to follow-up was 12.2% (50/409). Complete loss to follow-up was directly related to the number of absences (odds ratio; 95% confidence interval: 1.78; 1.49-2.12), and it was inversely related to age (0.97; 0.95-0.99). Complete loss to follow-up was less common among people who had met one of the researchers (0.51; 0.28-0.95) and among those with connective tissue disease (0.29; 0.09-0.98). For the multivariate logistic model the area under the ROC curve was 0.77.</p> <p>Conclusions</p> <p>Complete loss to follow-up after this health-education program can be predicted to some extent from data that are easy to collect (age, number of absences, and diagnosis). Also, face-to-face contact with a researcher deserves further study as a way of increasing participation in follow-up, and health-education programs should include it.</p