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

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 /

SEDENTARY BEHAVIOR IS INDEPENDENTLY ASSOCIATED WITH QUALITY OF LIFE IN PEOPLE WITH INFLAMMATORY BOWEL DISEASE

K. Taylor, P.W. Scruggs, C.A. Vella FACSM University of Idaho, Moscow, ID Inflammatory Bowel Disease (IBD), a severe gastrointestinal disease, affects 700,000 people in the US. IBD is thought to reduce quality of life (QOL) and currently there is no medical cure. PURPOSE: To investigate whether sedentary behavior was associated with QOL independent of moderate-vigorous physical activity (MVPA) in people with IBD, and whether resilience mediated this relationship. METHODS: 185 participants with IBD (81.6% female; 54.7% in remission; mean ± SD: age 37.2 ± 12.7 y; physical QOL 42.7 ± 9.3; mental QOL 38.4 ± 11.7; resilience 65.7 ± 13.7) completed an online-survey consisting of the Short Form-36 (SF-36), International Physical Activity Questionnaire (IPAQ), and the Connor-Davidson Resilience Scale (CD-RISC) to assess QOL, MVPA, and resilience, respectively. Multiple regression analyses examined the associations between sedentary behavior and physical and mental QOL, with MVPA, disease state, age, sex, and resilience as covariates. RESULTS: On average, participants spent 436.3 min/week sitting and 98.4 min/week in MVPA. Sedentary behavior was independently associated with physical QOL after adjusting for MVPA, disease state, age, and sex (R2=.28, β=-.22, p=.01). This association was slightly attenuated but remained significant when resilience was entered into the model (R2=.29, β=-.21, p=.03). Sedentary behavior was independently associated with mental QOL after adjusting for MVPA, disease state, age, and sex (R2=.21, β=-.23, p=.01). This association was no longer significant with the addition of resilience, suggesting it is a mediating variable (R2=.35, β=-.09, p=.29). CONCLUSIONS: We are the first to show that sedentary behavior is associated with both physical and mental QOL independent of MVPA in people with IBD. However, resilience mediates the relationship between MVPA and mental QOL in these patients. Thus, decreasing sedentary behavior and increasing resilience may be advantageous for improving QOL in people with IBD. Funded by the Gatorade Sports Science Institute (GSSI) Student Research Grant